Overview of the INEX 2013 Social Book Search
Track
Marijn Koolen1 , Gabriella Kazai2 , Michael Preminger3 , and Antoine Doucet4
1
University of Amsterdam, Netherlands
{marijn.koolen,kamps}@uva.nl
2
Microsoft Research, United Kingdom
a-gabkaz@microsoft.com
3
Oslo and Akershus University College of Applied Sciences, Norway
michaelp@hioa.no
4
University of Caen, France
doucet@info.unicaen.fr
Abstract. The goal of the INEX 2013 Social Book Search Track is to
evaluate approaches for supporting users in reading, searching, and nav-
igating collections of books based on book metadata, the full texts of
digitised books or associated user-generated content. The investigation
is focused around three tasks: 1) the Social Book Search (SBS) task in-
vestigates the complex nature of relevance in book search and the role of
traditional and user-generated book metadata in retrieval, 2) the Prove
It (PI) task evaluates focused retrieval approaches for searching pages
in books that can confirm or refute a given factual claim, 3) the Struc-
ture Extraction (SE) task evaluates automatic techniques for deriving
book structure from OCR text and layout information. Both the SBS
and SE tasks have a growing number of active participants, while the
PI task is only tackled by a small number of core groups. In the SBS
task, we extended last year’s investigation into the nature of book sug-
gestions from the LibraryThing forums and how they compare to book
relevance judgements. We found further support that such suggestions
are a valuable alternative to traditional test collections that are based
on top-k pooling and editorial relevance judgements. The PI task added
a further relevance criterion that pages should not only confirm or refute
a given factual claim, but should also come from an authoritative source
that is of the appropriate genre. The relevance assessments have not yet
commenced at the time of writing. The SE task has reached a record
number of active participants and has, for the first time, witnessed an
improvement in the state of the art.
1 Introduction
For centuries books were the dominant source of information, but how we ac-
quire, share, and publish information is changing in fundamental ways due to
the Web. The goal of the Social Book Search Track is to investigate techniques
to support users in searching and navigating the full texts of digitised books and
complementary social media as well as providing a forum for the exchange of
research ideas and contributions. Towards this goal the track is building appro-
priate evaluation benchmarks, complete with test collections for social, semantic
and focused search tasks. The track provides opportunities to explore research
questions around four key areas:
– Evaluation methodologies for book search tasks that combine aspects of
retrieval and recommendation,
– Information retrieval techniques for dealing with professional and user-generated
metadata,
– Semantic and focused retrieval techniques for searching collections of digi-
tised books, and
– Mechanisms to increase accessibility to the contents of digitised books.
Based around these main themes, the following three tasks were defined:
1. The Social Book Search (SBS) task, framed within the scenario of a user
searching a large online book catalogue for a given topic of interest, aims
at exploring techniques to deal with complex information needs—that go
beyond topical relevance and can include aspects such as genre, recency, en-
gagement, interestingness, and quality of writing—and complex information
sources that include user profiles, personal catalogues, and book descriptions
containing both professional metadata and user-generated content.
2. The Prove It (PI) task aims to evaluate focused retrieval approaches on
collections of digitised books, where users expect to be pointed directly at
relevant pages that may help to confirm or refute a given factual claim;
3. The Structure Extraction (SE) task aims at evaluating automatic techniques
for deriving structure from ocr and building hyperlinked table of contents.
In this paper, we report on the setup and the results of each of these tasks
at the 2013 inex@clef Lab. First, in Section 2, we give a brief summary of the
participating organisations. The SBS task is described in detail in Section 3, the
PI task in Section 4, and the SE task in Section 5. We close in Section 6 with a
summary and plans for 2014.
2 Participating Organisations
A total of 68 organisations registered for the track (compared with 55 in 2012
and 47 in 2011). At the time of writing, we counted 14 active groups (compared
with 5 in 2012 and 10 in 2011), see Table 1.
3 The Social Book Search Task
The goal of the Social Book Search (SBS) task is to evaluate the value of pro-
fessional metadata and user-generated content for book search on the Web and
Table 1. Active participants of the INEX 2013 Social Book Search Track, the
task(s) they were active in, and number of contributed runs (SBS = Social Book
Search, PI = Prove It , SE = Structure Extraction )
ID Institute Tasks Runs
4 University of Amsterdam, ILLC SBS, PI 6 SBS, 5 PI
4 University of Amsterdam, ILPS SBS 2 SBS
54 Royal School of Library and Information Science SBS 3 SBS
100 Oslo & Akershus University College of Applied Sciences SBS, PI 3 SBS, 6 PI
113 University of Caen SE 5 SE
123 LSIS / Aix-Marseille University SBS 3 SBS
147 National Taiwan Normal University SBS 6 SBS
180 Chaoyang University of Technology SBS 6 SBS
232 Indian School of Mines, Dhanbad SBS 5 SBS
280 University of Würzburg SE 1 SE
288 University of Innsbruck SE 1 SE
299 EPITA/LRDE SE 1 SE
303 Nankai University SE 1 SE
Microsoft Development Center Serbia SE 1 SE
Total 34 SBS, 11 PI, 10 SE
to develop and evaluate systems that can deal with both retrieval and recom-
mendation aspects, where the user has a specific information need against a
background of personal tastes, interests and previously seen books. Through so-
cial media, book descriptions have extended far beyond what is traditionally
stored in professional catalogues. Not only are books described in the users’
own vocabulary, but are also reviewed and discussed online, and added to online
personal catalogues of individual readers. This additional information is sub-
jective and personal, and opens up opportunities to aid users in searching for
books in different ways that go beyond the traditional editorial metadata based
search scenarios, such as known-item and subject search. For example, readers
use many more aspects of books to help them decide which book to read next
[7], such as how engaging, fun, educational or well-written a book is. In addi-
tion, readers leave a trail of rich information about themselves in the form online
profiles which contain personal catalogues of the books they have read or want
to read, personally assigned tags and ratings for those books and social network
connections to other readers. This results in a search task that may require a
different model than traditional ad hoc search [5] or recommendation.
The SBS task investigates book requests and suggestions from the Library-
Thing (LT) discussion forums as a way to model book search in a social envi-
ronment. The discussions in these forums show that readers frequently turn to
others to get recommendations and tap into the collective knowledge of a group
of readers interested in the same topic.
The task builds on the INEX Amazon/LibraryThing (A/LT) collection [1],
which contains 2.8 million book descriptions from Amazon, enriched with content
from LT. This collection contains both professional metadata and user-generated
content.
The SBS task aims to address the following research questions:
– Can we build reliable and reusable test collections for social book search
based on book requests and suggestions from the LT discussion forums?
– Can user profiles provide a good source of information to capture personal,
affective aspects of book search information needs?
– How can systems incorporate both specific information needs and general
user profiles to combine the retrieval and recommendation aspects of social
book search?
– What is the relative value of social and controlled book metadata for book
search?
3.1 Scenario
The scenario is that of a user turning to Amazon Books and LT to find books
to read, to buy or to add to their personal catalogue. Both services host large
collaborative book catalogues that may be used to locate books of interest.
On LT, users can catalogue the books they read, manually index them by
assigning tags, and write reviews for others to read. Users can also post messages
on discussion forums asking for help in finding new, fun, interesting, or relevant
books to read. The forums allow users to tap into the collective bibliographic
knowledge of hundreds of thousands of book enthusiasts. On Amazon, users can
read and write book reviews and browse to similar books based on links such as
“customers who bought this book also bought... ”.
Users can search online book collections with different intentions. They can
search for specific known books with the intention to obtain them (buy, down-
load, print). Such needs are addressed by standard book search services as offered
by Amazon, LT and other online bookshops as well as traditional libraries. In
other cases, users search for a specific, but unknown, book with the intention
of identifying it. Another possibility is that users are not looking for a specific
book, but hope to discover one or more books meeting some criteria. These cri-
teria can be related to subject, author, genre, edition, work, series or some other
aspect, but also more serendipitously, such as books that merely look interesting
or fun to read or that are similar to a previously read book.
3.2 Task description
The SBS task is to reply to a user request posted on a LT forum (see Section 3.5)
by returning a list of recommended books matching the user’s information need.
More specifically, the task assumes a user who issues a query to a retrieval
system, which then returns a (ranked) list of relevant book records. The user
is assumed to inspect the results list starting from the top, working down the
list until the information need has been satisfied or until the user gives up. The
retrieval system is expected to order the search results by relevance to the user’s
information need.
The user’s query can be a number of keywords, but also one or more book
records as positive or negative examples. In addition, the user has a personal
profile that may contain information on the user’s interests, list of read books
and connections with other readers. User requests may vary from asking for
books on a particular genre, looking for books on a particular topic or period
or books written in a certain style. The level of detail also varies, from a brief
statement to detailed descriptions of what the user is looking for. Some requests
include examples of the kinds of books that are sought by the user, asking for
similar books. Other requests list examples of known books that are related to
the topic but are specifically of no interest. The challenge is to develop a retrieval
method that can cope with such diverse requests.
The books must be selected from a corpus that consists of a collection of
curated and social book metadata, extracted from Amazon Books and LT, ex-
tended with associated records from library catalogues of the Library of Congress
and the British Library (see the next section). Participants of the SBS task are
provided with a set of book search requests and user profiles and are asked to
submit the results returned by their systems as ranked lists.
The SBS task, thus, combines aspects from retrieval and recommendation.
On the one hand, the task is akin to directed search, familiar from information
retrieval, with the requirement that returned books should be topically relevant
to the user’s information need described in the forum thread. On the other hand,
users may have particular preferences for writing style, reading level, knowledge
level, novelty, unusualness, presence of humorous elements and possibly many
other aspects. These preferences are to some extent reflected by the user’s read-
ing profile represented by the user’s personal catalogue. This catalogue contains
the books already read or earmarked for future reading, and may contain per-
sonally assigned tags and ratings. Such preferences and profiles are typical in
recommendation tasks, where the user has no specific information need, but is
looking for suggestions of new items based on previous preferences and history.
3.3 Submissions
We want to evaluate the book ranking of retrieval systems, specifically the top
ranks. We adopt the submission format of TREC, with a separate line for each
retrieval result, consisting of six columns:
1. topic id: the topic number, which is based on the LT forum thread number.
2. Q0: the query number. Unused, so should always be Q0.
3. isbn: the ISBN of the book, which corresponds to the file name of the book
description.
4. rank: the rank at which the document is retrieved.
5. rsv: retrieval status value, in the form of a score. For evaluation, results are
ordered by descending score.
6. run id: a code to identify the participating group and the run.
Participants are allowed to submit up to six runs, of which at least one should
use only the title field of the topic statements (the topic format is described in
Section 3.5). For the other five runs, participants could use any field in the topic
statement.
3.4 Data
To study the relative value of social and controlled metadata for book search,
we need a large collection of book records that contains controlled subject head-
ings and classification codes as well as social descriptions such as tags and re-
views, for a set of books that is representative of what readers are searching for.
We use the Amazon/LibraryThing (A/LT) corpus crawled by the University of
Duisburg-Essen for the INEX Interactive Track [1]. See https://inex.mmci.uni-
saarland.de/data/nd-agreements.jsp for information on how to gain access to
the corpus.
The collection consists of 2.8 million book records from Amazon, extended
with social metadata from LT. This set represents the books available through
Amazon. The records contain title information as well as a Dewey Decimal Clas-
sification (DDC) code (for 61% of the books) and category and subject infor-
mation supplied by Amazon. We note that for a sample of Amazon records we
noticed the subject descriptors to be noisy, with a number of inappropriately
assigned descriptors that seem unrelated to the books.
Each book is identified by an ISBN. Since different editions of the same work
have different ISBNs, there can be multiple records for a single intellectual work.
Each book record is an XML file with fields like isbn, title, author, publisher,
dimensions, numberofpages and publicationdate. Curated metadata comes in the
form of a Dewey Decimal Classification in the dewey field, Amazon subject
headings in the subject field, and Amazon category labels in the browseNode
fields. The social metadata from Amazon and LT is stored in the tag, rating,
and review fields. The full list of fields is shown in Table 2.
To ensure that there is enough high-quality metadata from traditional library
catalogues, we extended the A/LT data set with library catalogue records from
the Library of Congress (LoC) and the British Library (BL). We only use library
records of ISBNs that are already in the A/LT collection. These records contain
formal metadata such as title information (book title, author, publisher, etc.),
classification codes (mainly DDC and LCC) and rich subject headings based on
the Library of Congress Subject Headings (LCSH).5 Both the LoC records and
the BL records are in MARCXML6 format. There are 1,248,816 records from the
LoC and 1,158,070 records in MARC format from the BL. Combined, there are
2,406,886 records covering 1,823,998 of the ISBNs in the A/LT collection (66%).
Although there is no single library catalogue that covers all books available on
5
For more information see: http://www.loc.gov/aba/cataloging/subject/
6
MARCXML is an XML version of the well-known MARC format. See: http://www.
loc.gov/standards/marcxml/
Table 2. A list of all element names in the book descriptions
tag name
book similarproducts title imagecategory
dimensions tags edition name
reviews isbn dewey role
editorialreviews ean creator blurber
images binding review dedication
creators label rating epigraph
blurbers listprice authorid firstwordsitem
dedications manufacturer totalvotes lastwordsitem
epigraphs numberofpages helpfulvotes quotation
firstwords publisher date seriesitem
lastwords height summary award
quotations width editorialreview browseNode
series length content character
awards weight source place
browseNodes readinglevel image subject
characters releasedate imageCategories similarproduct
places publicationdate url tag
subjects studio data
Amazon, we reason that these combined library catalogues can improve both
the quality and quantity of professional book metadata. Indeed, with the LoC
and BL data sets combined, 79% of all ISBNs in the original A/LT corpus now
have a DDC code. In addition, the LoC data set also has LCC codes for 44%
of the records in the collection. With only the A/LT data, 57% of the book
descriptions have at least one subject heading, but with the BL and LoC data
added, this increases to 80%. Furthermore, the A/LT data often has only a
single subject heading per book, whereas in the BL and LoC data sets, book
descriptions typically have 2–4 headings (average 2.96). Thus, the BL and LoC
data sets increase the coverage of curated metadata, such that the vast majority
of descriptions in our data set include professionally assigned classification codes
and subject headings.
ISBNs and intellectual works Each record in the collection corresponds
to an ISBN, and each ISBN corresponds to a particular intellectual work. An
intellectual work can have different editions, each with their own ISBN. The
ISBN-to-work relation is a many-to-one relation. In many cases, we assume the
user is not interested in all the different editions, but in different intellectual
works. For evaluation we collapse multiple ISBN to a single work. The highest
ranked ISBN is evaluated and all lower ranked ISBNs of the same work ignored.
Although some of the topics on LibraryThing are requests to recommend a
particular edition of a work—in which case the distinction between different
ISBNs for the same work are important—we ignore these distinctions to make
evaluation easier. This turns edition-related topics into known-item topics.
However, one problem remains. Mapping ISBNs of different editions to a
single work is not trivial. Different editions may have different titles and even
have different authors (some editions have a foreword by another author, or a
translator, while others have not), so detecting which ISBNs actually represent
the same work is a challenge. We solve this problem by using mappings made
by the collective work of LibraryThing members. LT members can indicate that
two books with different ISBNs are actually different manifestations of the same
intellectual work. Each intellectual work on LibraryThing has a unique work ID,
and the mappings from ISBNs to work IDs is made available by LibraryThing.7
The mappings are not complete and might contain errors. Furthermore, the
mappings form a many-to-many relationship, as two people with the same edition
of a book might independently create a new book page, each with a unique work
ID. It takes time for members to discover such cases and merge the two work
IDs, which means that at any time, some ISBNs map to multiple work IDs even
though they represent the same intellectual work. LibraryThing can detect such
cases but, to avoid making mistakes, leaves it to members to merge them. The
fraction of works with multiple ISBNs is small so we expect this problem to have
a negligible impact on evaluation.
3.5 Information needs
LT users discuss their books on the discussion forums. Many of the topic threads
are started with a request from a member for interesting, fun new books to read.
Users typically describe what they are looking for, give examples of what they like
and do not like, indicate which books they already know and ask other members
for recommendations. Members often reply with links to works catalogued on
LT, which have direct links to the corresponding records on Amazon. These
requests for recommendations are natural expressions of information needs for
a large collection of online book records. We use a sample of these forum topics
to evaluate systems participating in the SBS task.
Each topic has a title and is associated with a group on the discussion forums.
For instance, topic 99309 in Figure 1 has the title Politics of Multiculturalism
Recommendations? and was posted in the group Political Philosophy. The books
suggested by members in the thread are collected in a list on the side of the
topic thread (see Figure 1). A feature called touchstone can be used by members
to easily identify books they mention in the topic thread, giving other read-
ers of the thread direct access to a book record in LT, with associated ISBNs
and links to Amazon. We use these suggested books as initial relevance judge-
ments for evaluation. In the rest of this paper, we use the term suggestion to
refer to a book that has been identified in a touchstone list for a given forum
topic. Since all suggestions are made by forum members, we assume they are
7
See: http://www.librarything.com/feeds/thingISBN.xml.gz
Fig. 1. A topic thread in LibraryThing, with suggested books listed on the right
hand side.
valuable judgements on the relevance of books. Additional relevance informa-
tion can be gleaned from the discussions on the threads. Consider, for example,
topic 1299398 . The topic starter first explains what sort of books he is looking
for, and which relevant books he has already read or is reading. Other members
post responses with book suggestions. The topic starter posts a reply describing
which suggestions he likes and which books he has ordered and plans to read.
Later on, after some more discussions, the topic starter provides feedback on the
suggested books that he has now read. Such feedback can be used to estimate
the relevance of a suggestion to the user.
User profiles and personal catalogues From LT we can not only extract the
information needs of social book search topics, but also the rich user profiles of
the topic starters, which contain information on which books they have in their
personal catalogue on LT, which ratings and tags they assigned to them and
a social network of friendship relations, interesting library relations and group
memberships. These profiles may provide important signals on the user’s topical
and genre interests, reading level, which books they already know and which ones
they like and don’t like. These profiles were scraped from the LT site, transformed
into xml and made available to participants. This adds a recommendation aspect
to the task. One of the research questions of the SBS task is whether this profile
information can help systems in identifying good suggestions.
Although the user expresses her information need in some detail in the dis-
cussion forum, she may not describe all aspects she takes into consideration
8
URL: urlhttp://www.librarything.com/topic/129939
Table 3. User profile statistics of the topic starters. Days of membership is the
number of days between becoming LT member and posting the topic on the
forum
Type N min max median mean stdev
Days of membership 373 0 2093 215 413.08 476.50
Friends 380 0 135 2 7.21 15.28
Interesting libraries 380 0 266 0 10.33 26.20
Groups 380 0 10 6 5.64 4.18
Books 380 0 6294 104 505.78 1002.35
Ratings 380 0 2771 15 163.59 389.85
Tags 380 0 44,283 191 1549.19 4160.44
when selecting books. This may partly be because she wants to explore different
options along different dimensions and therefore leaves some room for different
interpretations of her need. Another reason might be that some aspects are not
related directly to the topic at hand but may be latent factors that she takes
into account with selecting books in general.
What information is available in the LT profile of the topic starter? First,
there are the connections to other members, which come in two kinds, friendship
connections and interesting library connections. The former is a connection with
another LT member she knows, the latter a connection to an LT member whose
personal catalogue she finds interesting. Then there are the group membership
connections to the discussion groups on the LT forums. These signal some of her
(book-related) interests. Finally, there is the personal catalogue, which contains
basic information on the books that the user has added to her personal LT
catalogue, when she added each book, as well as any tags and ratings she assigned
to each individual book. Basic statistics on the number of connections, books,
ratings and tags per user profile is given in Table 3. In the top row we see
the number of days between the topic starter registering to LT and posting the
topic on the forum. On average, users build up their profiles for over a year before
they ask for book suggestions, although the distribution is skewed. Most book
recommendation requests are posted within 215 days of registering to LT, with
54 topics (14%) posted on the day of registration. The latter topics may actually
be the reason that the topic starter registered to LT. All frequency distributions,
with the exception of group connections, are heavily skewed, with a small number
of high end outliers causing the mean to be much higher than the median. The
number of groups shown on the profile is cut-off by LT at 10 groups, which
means our profiles miss some group connections for members with more than 10
groups. Most topic starters have a small number of friends and are registered
to up to half a dozen discussion groups. Topic starters have around 100 books
in their catalogues, almost twice as many tags and only few ratings, although
some users have much bigger and more heavily tagged catalogues. There is a
lot of variation in the profiles in all aspects mentioned in Table 3. Especially
for profiles with many books, tags and connections, recommendation algorithms
may need to be tuned to the parts of the profile that are relevant to the specific
information need of the topic thread.
In the following sections, we describe the procedures for topic selection (Sec-
tion 3.6) and for suggestion annotation (Section 3.7) procedure, then how we
used the annotations to assign relevance values to the suggestions (Section 3.8).
3.6 Topic selection
Three students from the Royal School of Library and Information Science were
paid to annotate the narratives of a sample of LT forum topics. We created an
interface to help them to 1) select topic threads that are about book search
information needs (as opposed to anything else that people discuss on the LT fo-
rums), 2) annotate the selected topics describing the type of information need—
are users looking for books about a particular topic, in a certain genre, by a
certain author, etc.—and 3) annotate the suggestions provided by LT members
in the thread. The latter included questions on whether the suggestors have
read the suggested books and what their attitudes were towards the books, i.e.
positive recommendations vs negative mentions.
An initial set of 9,401 topic threads containing touchstones was made avail-
able to the students. Of those, the students annotated over 1100 topics, of which
386 (35%) were identified as topics with a book search information need. From
the 386, six topics contain no suggestions to any of the books in our A/LT col-
lection. Although all 386 topics were distributed to participants, these 6 topics
are discarded from the evaluation.
Topics can have complex information needs, with a combination of multiple
relevance aspects. Traditionally, in ir, the focus has been on what a document
is about, but in book search there are often many other aspects of relevance.
Reuter [7] identified 7 general categories of relevance aspects for book search, to
which we added the category of known-item information needs:
Metadata books with a certain title or by a certain author, editor, illustrator,
publisher, in a particular format, or written
Accessibility the language, length or level of difficulty of a book,
Content aspects such as topic, plot, genre, style or comprehensiveness of a
book,
Engagement books that fit a particular mood or interest, or books that are
considered high quality or provide a particular reading experience,
Novelty books with novel content for the reader, books that are unusual,
Familiarity similar to known book or related to previous experience,
Socio-Cultural books related the user’s socio-cultural background or values,
books that are popular or obscure.
Known-item description of known book to identify title and/or author, or
published in certain year or period,
In the second step, annotators had to indicate which aspects of relevance the
topics relate to. Annotators could select multiple relevance categories. For exam-
ple, for topic 99309 on the politics of muliticulturalism, the topic starter asks for
Table 4. Distribution of relevance aspect types over topics
Aspect # %
Metadata 138 36
Accessibility 63 17
Content 314 83
Engagement 107 28
Novelty 13 3
Familiarity 168 44
Socio-Cultural 58 15
Known-item 92 24
Table 5. Distribution of number of relevance aspects per topic
# Aspects # topics (%)
1 57 (15%)
2 144 (38%)
3 134 (35%)
4 37 (10%)
5 6 (2%)
6 2 (1%)
suggestions about a particular topic—i.e., content relevance—but also asks for
books that add something new to what he has already read on the topic—i.e.,
novelty. The distribution of the relevance aspects in the topic set is shown in
Table 4. Book search information needs on the LT forums almost always (83%
of the 380 topics) contain content aspect. This reinforces the traditional choice
in designing best match retrieval models around aspects of document content.
Metadata aspects, such as book title and author, are present in 36% of the data
set. Other important aspects are familiarity (44%) and engagement (28%) and
known-item (24%). Looking for books similar to certain books a user has read
is the task of item-based recommender systems, such as that offered by Amazon
(’customers who bought this book also bought...’). It reinforces our interpretation
of LT forum book search needs as a task that combines aspects of retrieval and
recommendation. Engagement is something that is hard to express in a search
engine query. For instance, how can a user search for text books that are funny
or high-brow literature that is scary, or books that challenge the reader’s own
views on a topic? So it is not surprising that readers instead turn to other readers
to ask for suggestions. The same holds for read or ‘heard about’ books for which
the user only recalls some aspect of the plot, or the some attributes of certain
characters. Book search services are of limited use for such known-item topics,
but forum members might be able to help out. Accessibility, novelty and socio-
cultural aspects are less prominent in our sample set. Only 15% of the topics
have a single relevance aspect (see Table 5). The vast majority topics represent
complex information needs—most topics have 2 or 3 relevance aspects, 144 and
134 topics respectively—with a mean number of 2.47 aspects per topic.
In addition to the above, annotators had to indicate whether the request
was for fiction, non-fiction or both and they had to provide a search query that
they would use with a book search engine. We hope that these queries better
express the information need than some of the topic thread titles, some of which
do not describe the information need at all. Of the 380 topics, 175 (46%) asked
for suggestions on fiction books, 49 (13%) on non-fiction, 55 (14%) on both
fiction and non-fiction, and for 101 topics (27%) the annotator could not tell.
The fraction of non-fiction topics is lower than last year (49%). We assume that
this difference is caused by giving the annotators the option to indicate they
were not sure, whereas in last year’s topic selection procedure, we always choose
between fiction, non-fiction or both.
Figure 1 shows an annotated topic (topic 99309) as an example:
Politics of Multiculturalism
Politics of Multiculturalism Recommendations?
Political Philosophy
steve.clason
I’m new, and would appreciate any recommended reading on
the politics of multiculturalism. Parekh
’s Rethinking Multiculturalism: Cultural
Diversity and Political Theory (which I just finished) in the end
left me unconvinced, though I did find much of value I thought he
depended way too much on being able to talk out the details later. It
may be that I found his writing style really irritating so adopted a
defiant skepticism, but still... Anyway, I’ve read
Sen, Rawls,
Habermas, and
Nussbaum, still don’t feel like I’ve
wrapped my little brain around the issue very well and would
appreciate any suggestions for further anyone might offer.
3.7 Suggestion annotations
Finally, annotators had to label each book suggestion provided by LT members
(including any provided by the topic starter). They had to indicate whether the
suggestor has read the book. For the has read question, the possible answers
were Yes, No, Can’t tell and It seems like this is not a book. They also had
to judge the attitude of the suggestor towards the book. Possible answers were
Positively, Neutrally, Negatively, Not sure or This book is not mentioned as a
relevant suggestion! The latter can be chosen when someone mentions a book
for another reason than to suggest it as a relevant book for the topic of request.
The 380 topic threads were started by 378 different members in 347 different
discussion groups, containing altogether 2,568 messages from 1,109 different LT
Table 6. Distribution of participants, messages and suggestions per thread,
number of topics in which participants participate and the number of suggestions
per message
description # min. max. med. mean std.dev
participants/topics 380 1 53 3 5.39 5.75
messages/topic 380 1 107 4 6.76 8.73
touchstones/topic 380 1 234 8 15.87 22.73
topics/participant 1109 1 68 2 4.26 6.06
touchstones/message 2568 1 204 10 17.55 25.83
touchstones/message 2568 1 29 1 2.35 2.55
Table 7. Distribution of annotation labels over the answer categories on whether
the suggestor has read the suggestion and the suggestors attitude towards to book
Attitude
Read pos neu neg not sure non sug unticked total
yes 2757 700 134 116 87 0 3794
no 198 263 15 11 15 0 502
can’t tell 136 1224 7 97 107 0 1571
non book 2 0 0 0 0 160 162
total 3093 2187 156 224 209 160 6029
members. The distribution of participants, messages and touchstones per topic
as shown in Table 6. On average, each thread has 5.39 participants (median is 3),
6.76 messages (median 4) and 15.87 touchstones (median 8). The distributions
are all skewed to the right. Most participants contribute touchstones to multiple
topics (topics/participant). Messages with touchstones typically contain only
one suggestion (median touchstones per message is 1), although some messages
contain many touchstones (58 messages contain 10 touchstones or more).
The relationship between having read the book and the suggestor’s attitude
towards the book is shown in Table 7. In the majority of cases (63%) members
suggest books that they have read. It is rather rare for suggestors to state that
they have not read a suggested book (8%). More often, suggestors do not reveal
whether they have read the book or not (26%). Books mentioned in response to
a book search request are often presented in a positive (51%) or neutral (36%)
way. Both positive and negative suggestions tend to come from members who
have read the books. When books are mentioned in a neutral way, it is often
difficult to tell whether the book has been read by the suggestor, although a
third of the neutral mentions comes from members who have read the book.
There are 162 touchstones that do not refer to books but to author names. In
almost all such cases (160 out of 162), annotators skipped the question regarding
the attitude of the suggestor towards the book. In the two remaining cases, the
annotator indicated the suggestor mentioned the author in a positive way.
How do the attitude labels relate to the books mentioned by the forum mem-
bers? Are all books mentioned only once or is there a lot of discussion in many
threads? Do members agree on which books are good suggestions and which
ones are not? Within a thread, books can be mentioned multiple times, by a
single member or by multiple members. The 6029 touchstones represent 5092
books. Most books, 4480 or 88% were mentioned only once in the thread, 612
(12%) were mentioned twice or more and 175 (3%) were mentioned three times
or more. Of the 4480 books mentioned only once, 2292 (51%) are mentioned
positively, 1683 (38%) neutrally, 85 (2%) negatively and 420 (9%) are labelled
as either not sure, non-suggestion or were skipped because they were author
names misidentified as book titles. Books mentioned by forum members tend to
be positive suggestions. Of the 612 books mentioned multiple times, attitudes
are mostly all positive (230 books, or 38%)) or a mix of positive and neutral
(194 or 32%). For 116 books (19%), all attitudes are neutral. Only for 31 books
(5%) there is real disagreement—some are positive, some are negative—and for
26 books (4%) attitudes are all negative or mixed neutral and negative. For 15
books (2%) the annotators cannot tell the attitude or indicated the touchstone is
not actually a book. In other words, when books are mentioned multiple times,
forum members rarely disagree with each other and are mostly positive.
All in all, in response to a book search request, members suggest mostly
books they have read and often in a positive way. This supports our choice of
using forum suggestions as relevance judgements.
3.8 Operationalisation of forum judgement labels
The annotated suggestions labels were used to determine the relevance value of
each book suggestion in the thread. Because some of the books mentioned in the
forums are not part of the 2.8 million books in our collection, we first removed
from the suggestions any books that are not in the INEX A/LT collection, which
leaves 4572 out of 5092 books (90%). For these 4572 books we derive relevance
values.
Forum members can mention books for many different reasons. We want the
relevance values to distinguish between books that were mentioned as positive
recommendations, negative recommendations (books to avoid), neutral sugges-
tions (mentioned as possibly relevant but not necessarily recommended) and
books mentioned for some other reason (not relevant at all). We also want to
differentiate between recommendations from members who have read the book
they recommend and members who have not. We assume the recommendation
to be of more value to the searcher if it comes from someone who has actually
read the book. For the mapping to relevance values, we refer to the first mention
of work as the suggestion and subsequent mentions of the same work as replies.
We use has read when the forum members have read the book they mention
and not read when they have not. Furthermore, we use a number of simplifying
assumptions:
– When the annotator was not sure if the person mentioning a book has read
it, we treat it as not read. We argue that for the topic starter it is not clear
there is a difference in value of such recommendations.
– When the annotator was not sure if the person mentioning a book is positive,
negative or neutral, we treat it as neutral. Again, for the topic starter there
is no clear signal that there is difference in value.
– has read recommendations overrule not read recommendations. Someone who
has read the book is in a better position to judge a book than of someone
who has not.
– positive and negative recommendations neutralise each other. I.e. a positive
and a negative recommendation together are the same as two neutral recom-
mendations.
– If the topic starter has read a book she mentions, the relevance value is
rv = 0. We assume such books have no value as suggestions.
– The attitude of the topic starter towards a book overrules those of others.
The system should retrieve books for the topic starter, not for others.
– When forum members mention a single work multiple times, we use the last
mention as judgement.
With the following decision tree we determine from which forum members want
to use the judgements to derive relevance values:
1. Book mentioned by single member → use that member’s judgement
2. Book mentioned by multiple members
2.1 topic starter mentions book
2.1.1 topic starter only suggests neutrally → use replies of others (2.2)
2.1.1 topic starter suggests positively/negatively → use starter judgement
2.1.1 topic starter replies → use starter judgement
2.2 topic starter does not mention book
2.2.2 members who have read the book suggest/reply → use has read
judgements
2.2.2 no member who suggests/replies about a book has read it → use all
judgements
Once the judgements per suggested book are determined, we map the annotated
judgements to relevance values. The base relevance value of a book that is men-
tioned in the thread is rv = 2. The values are modified according to the following
scheme:
1. single judgement
1.1 starter has read judgement → rv = 0
1.2 starter has not read judgement
1.2.2 starter positive → rv = 8
1.2.2 starter neutral → rv = 2
1.2.2 starter negative → rv = 0
1.3 other member has read judgement
1.3.3 has read positive → rv = 4
1.3.3 has read neutral → rv = 2
1.3.3 has read negative → rv = 0
1.4 other member has not read judgement
1.4.4 not read positive → rv = 3
1.4.4 not read neutral → rv = 2
1.4.4 not read negative → rv = 0
2. multiple judgements
2.1 multiple has read judgements
2.1.1 some positive, no negative → rv = 6
2.1.1 #positive > #negative → rv = 4
2.1.1 #positive == #negative → rv = 2
2.1.1 all neutral → rv=2
2.1.1 #positive < #negative → rv = 1
2.1.1 no positive, some negative → rv = 0
2.2 multiple not read judgements
2.2.2 some positive, no negative → rv = 4
2.2.2 #positive > #negative → rv = 3
2.2.2 #positive == #negative → rv = 2
2.2.2 all neutral → rv=2
2.2.2 #positive < #negative → rv = 1
2.2.2 no positive, some negative → rv = 0
This results in graded relevance values with seven possible values (0, 1, 2, 3, 4,
6, 8). For the 380 topics, there are 4572 relevance values (see Table 8), with 438
from judgements by the topic starter, and 4134 from the judgements of other
members. Of these, 3892 are based on single judgements and 242 on judgements
from multiple other members. The topic starters contribute only 10% of the
relevance values and half of them on books they have not read. The vast majority
of values come from single other forum members (3892 or 85%). Also, 3088
relevance values are based on judgements from members who have read the
book (67%).
3.9 Evaluation
This year eight teams together submitted 34 runs (see Table 1). The official
evaluation measure for this task is ndcg@10. It takes graded relevance val-
ues into account and concentrates on the top retrieved results. The results are
Table 8. Statistics on the types of member judgements on which the relevance
values are based
has read not read total
creator 218 220 438
other single 2666 1226 3892
other multi 204 38 242
total 3088 1484 4572
Table 9. Evaluation results for the official submissions. Best scores are in bold.
Runs starting with * are manual runs
Group Run ndcg@10 P@10 mrr map
RSLIS run3.all-plus-query.all-doc-fields 0.1361 0.0653 0.2407 0.1033
UAms (ILLC) inex13SBS.ti qu.bayes avg.LT rating 0.1331 0.0771 0.2437 0.0953
UAms (ILLC) inex13SBS.ti qu gr na.bayes avg... 0.1320 0.0668 0.2355 0.0997
RSLIS run1.all-topic-fields.all-doc-fields 0.1295 0.0647 0.2290 0.0949
UAms (ILLC) inex13SBS.ti qu gr na 0.1184 0.0555 0.2169 0.0926
UAms (ILLC) inex13SBS.ti qu 0.1163 0.0647 0.2197 0.0816
ISMD *run ss bsqstw stop words free member... 0.1150 0.0479 0.1930 0.0925
UAms (ILLC) inex13SBS.qu.bayes avg.LT rating 0.1147 0.0661 0.2092 0.0794
ISMD *run ss bsqstw stop words free 2013 0.1147 0.0468 0.1936 0.0924
UAms (ILLC) inex13SBS.ti.bayes avg.LT rating 0.1095 0.0634 0.2089 0.0772
ISMD ism run ss free text 2013 0.1036 0.0426 0.1674 0.0836
ISMD *run ss bsqstw 2013 0.1022 0.0416 0.1727 0.0830
ISMD *run ss bsqstw stop words free query... 0.0940 0.0495 0.1741 0.0675
NTNU aa LMJM3 0.0832 0.0405 0.1537 0.0574
NTNU az LMJM3 0.0814 0.0376 0.1534 0.0504
NTNU az BM25 0.0789 0.0392 0.1517 0.0540
NTNU aa BM25 0.0780 0.0366 0.1426 0.0485
UAms (ILPS) UAmsRetTbow 0.0664 0.0355 0.1235 0.0483
UAms (ILPS) indri 0.0645 0.0347 0.1245 0.0445
NTNU qa LMD 0.0609 0.0345 0.1249 0.0396
LSIS/AMU score file mean R 2013 reranked 0.0596 0.0324 0.1200 0.0462
NTNU qz LMD 0.0577 0.0342 0.1186 0.0366
LSIS/AMU score file SDM HV 2013 reranked 0.0576 0.0292 0.1252 0.0459
LSIS/AMU resul SDM 2013 0.0571 0.0297 0.1167 0.0459
RSLIS run2.query.all-doc-fields 0.0401 0.0208 0.0728 0.0314
CYUT Run4.query.RW 0.0392 0.0287 0.0886 0.0279
CYUT Run6.query.reviews.RW 0.0378 0.0284 0.0858 0.0244
CYUT Run2.query.Rating 0.0376 0.0284 0.0877 0.0259
CYUT Run1.query.content-base 0.0265 0.0147 0.0498 0.0220
CYUT Run5.query.reviwes.content-base 0.0254 0.0153 0.0457 0.0209
CYUT Run3.query.RA 0.0170 0.0087 0.0437 0.0166
OUC sb ttl nar 10000 0.5 0.0100 0.0071 0.0215 0.0076
OUC sb ttl nar 0.4 0.0044 0.0029 0.0104 0.0031
OUC sb ttl nar 2500 0.0039 0.0032 0.0097 0.0032
shown in Table 9. None of the groups used user profile information for the runs
they submitted. The best performing run is run3.all-plus-query.all-doc-fields by
RSLIS, which used all topic fields combined against an index containing all
available document fields. The second best group is UAms (ILLC) with run
inex13SBS.ti qu.bayes avg.LT rating, which uses only the topic titles and mod-
erated query ran against an index containing the title information fields (ti-
tle, author, edition, publisher, year), user-generated content fields (tags, reviews
and awards) and the subject headings and Dewey decimal classification titles
from the British Library and Library of Congress. The retrieval score of each
book was then multiplied by a prior probability based on the Bayesian aver-
age of LT ratings for that book. The third group is ISMD, with manual run
run ss bsqstw stop words free member free 2013 (to make the table fit on the
page, it is shortened to run ss bsqstw stop words free member...). This run is
generated after removing Book Search Query Stop Words (bsqstw), standard
stop words and the member field from the topics and running against an index
where stop words are removed and the remaining terms are stemmed with the
Krovetz stemmer. If we ignore the manual runs, ismd is still the third group
with the fully automatic run ism run ss free text 2013, which is generated using
free text queries on Krovetz stemmed and stop words removed index.
Many teams used similar approaches, with query representations based on a
combination of topic fields and indexes based on both professional and user-
generated metadata. It seems that advanced models were implemented that
combining topic statements with profile information or that treat professional
metadata differently from user-generated content. This may be due to the late
release the topics and profiles and the submission deadline being early because
of changes in the schedule of clef. It is also possible that for most participants
this task is felt to be a retrieval task modelled after standard TREC tasks, so
there is little attention for recommendation aspects.
4 The Prove It (PI) Task
The goal of this task was to investigate the application of focused retrieval ap-
proaches to a collection of digitised books. The scenario underlying this task
is that of a user searching for specific information in a library of books that
can provide evidence to confirm or reject a given factual statement. Users are
assumed to view the ranked list of book parts, moving from the top of the list
down, examining each result. No browsing is considered (only the returned book
parts are viewed by users).
Participants could submit up to 10 runs. Each run could contain, for each
of the 83 topics (see Section 4.2), a maximum of 1,000 book pages estimated
relevant to the given aspect, ordered by decreasing value of relevance.
A total of 11 runs were submitted by 2 groups (6 runs by OUAC (ID=100)
and 5 runs by University of Amsterdam (ID=4)), see Table 1.
4.1 The Digitized Book Corpus
The track builds on a collection of 50,239 out-of-copyright books9 , digitised by
Microsoft. The corpus is made up of books of different genre, including his-
tory books, biographies, literary studies, religious texts and teachings, reference
works, encyclopaedias, essays, proceedings, novels, and poetry. 50,099 of the
books also come with an associated MAchine-Readable Cataloging (MARC)
record, which contains publication (author, title, etc.) and classification infor-
mation. Each book in the corpus is identified by a 16 character long bookID – the
name of the directory that contains the book’s OCR file, e.g., A1CD363253B0F403.
The OCR text of the books has been converted from the original DjVu for-
mat to an XML format referred to as BookML, developed by Microsoft De-
velopment Center Serbia. BookML provides additional structure information,
including markup for table of contents entries. The basic XML structure of a
typical book in BookML is a sequence of pages containing nested structures
of regions, sections, lines, and words, most of them with associated coordinate
information, defining the position of a bounding rectangle ([coords]):
[...]
[...]
[...]
BookML provides a set of labels (as attributes) indicating structure informa-
tion in the full text of a book and additional marker elements for more complex
structures, such as a table of contents. For example, the first label attribute
in the XML extract above signals the start of a new chapter on page 1 (la-
bel=“PT CHAPTER”). Other semantic units include headers (SEC HEADER),
footers (SEC FOOTER), back-of-book index (SEC INDEX), table of contents
(SEC TOC). Marker elements provide detailed markup, e.g., for table of con-
tents, indicating entry titles (TOC TITLE), and page numbers (TOC CH PN),
etc.
The full corpus, totaling around 400GB, was made available on USB HDDs.
In addition, a reduced version (50GB, or 13GB compressed) was made available
for download. The reduced version was generated by removing the word tags
and propagating the values of the val attributes as text content into the parent
(i.e., line) elements.
9
Also available from the Internet Archive (although in a different XML format)
4.2 Topics
In recent years we have had a topic-base of 83 topics, and for 30 of them we have
collected relevance judgments using crowdsourcing through Amazon Mechanical
Turk [4, 6]. This year we added a new relevance criterium, namely appropriate-
ness. It is not enough that a page confirms or refutes a fact, it should also come
from a book that is trusted and of an appropriate genre. For a fact about Dar-
win’s life, a famous biography on Darwin would be a more appropriate source
than an obscure textbook on biology. Book pages are judged on two levels: 1)
the extent to which a page confirms or refutes the factual claim, determined by
how many of the atomic aspects of the claim are confirmed/refuted, and 2) the
appropriateness of the book of the page is a part.
Aspect relevance Last year we introduced aspect relevance [6], where complex
statements were broken down into atomic parts, which could be judge more easily
individually. The overall relevance score of page for the whole statement would
be the sum of relevance scores for the atomic parts.
To divide each topic into its primitive aspects (a process we refer to as ”as-
pectisation”) we developed a simple web-application with a database back-end,
to allow anyone to aspectise topics. This resulted in 30 aspectised topics. The
judgements were collected last year. For each page being assessed for confirma-
tion / refutation of a topic, the assessor is presented with a user interface similar
to Figure 2
Fig. 2. An illustration of the planned assessment interface
This means that we go from a discrete (confirms / refute / none ) assessment
to a graded assessment, where a page may e.g. be assessed by a certain as 33
percent confirming a topic, if one of three aspects is judged as confirmed by
him/her for that page. For the current assessment we have prepared 30 topics,
for which the number of aspects range from 1 (very simple statements) to 6 per
topic with an average of 2,83 aspects per topic.
Appropriateness For this year, the new assessment phase should establish
whether the source books for the top-10 pages per topic are appropriate. At
the time of writing, the assessment interface is not yet ready, but we expect to
run this phase in the summer of 2013. The interface will present assessors with
one of the top-10 pages, and provide them with the ability to browse through
the rest of the book both via forward/backward buttons, an interactive table of
contents in a sidebar as well as a page number box in which they can indicate
to which specific page they want to jump. For each source book of the top-10
pooled pages, the assessor has to judge the appropriateness of the book. This
appropriateness score is then propagated to all its pages that are part of the
judgement pool.
4.3 Collected Relevance Assessments
As for the 2012 experiments, one hundred pages were pulled from the top-10
results of participant submissions for each topic. Assessments for each page and
statement were collected from three assessors. New for this year, we also ask
assessors to assess weather the book a presented page belongs to is appropriate
for the task of confirming or refuting the statement. appropriateness is based on
the book’s genre or topic.
4.4 Evaluation Measures and Results
Result publication is awaiting the conclusion of the relevance assessment process.
5 The Structure Extraction (SE) Task
As in previous years, the goal of the SE task was to test and compare automatic
techniques for extracting structure information from digitised books and building
a hyperlinked table of contents (ToC). The task was motivated by the limita-
tions of current digitisation and OCR technologies that produce the full text of
digitised books with only minimal structure markup: pages and paragraphs are
usually identified, but more sophisticated structures, such as chapters, sections,
etc., are typically not recognised.
In 2013, the task was run for the third time as a competition of the Interna-
tional Conference on Document Analysis and Recognition (ICDAR). Full details
are presented in the corresponding specific competition description [3]. This year,
the main novelty was that ground truthing was performed by an independent
provider. This granted higher consistency and set participants free from ground
truthing duties, which were a known drawback of participating to the task. The
ground truth for the 2013 competition is already available online10 .
10
https://doucet.users.greyc.fr/StructureExtraction/training/
Organization Submitted runs First submission
Elsevier 0 -
EPITA (France) 1 2013
INRIA (France) 0 -
Microsoft Development Center (Serbia) 1 2009
Nankai University (PRC) 1 2011
NII Tokyo (Japan) 0 -
University of Caen (France) 5 2009
University of Innsbruck (Austria) 1 2013
University of Würzburg (Germany) 1 2013
Table 10. Active participants of the Structure Extraction task.
Participation
Following the call for participation issued in January 2013, 9 organizations reg-
istered. As in previous competitions, several participants expressed interest but
renounced due to time constraints. Of the 9 organizations that signed up, 6
submitted runs. This promising increase in active participants (6 out of 9), com-
pared with previous years (4 out of 11), is likely a result of available training
data and the removed obligation on creating ground truth ToCs.
Results
As in previous years [2], the 2013 task permitted to make manual annotations
available to the community. The efforts of the 2013 round gave way to the addi-
tion of 967 new annotated book ToCs to the existing 1,037, nearly doubling the
amount of available test data.
A summary of the performance of all the submitted runs is given in Table 11.
The Structure Extraction task was launched in 2008 to compare automatic
techniques for extracting structure information from digitised books. While the
construction of hyperlinked ToCs was originally thought to be a first step on the
way to the structuring of digitised books, it turns out to be a much tougher nut
to crack than initially expected.
Future work aims to investigate into the usability of the extracted ToCs.
In particular we wish to use qualitative measures in addition to the current
precision/recall evaluation. The vast effort that this requires suggests that this
can hardly be done without crowdsourcing. We shall naturally do this by building
on the experience of the Book Search tasks described earlier in this paper.
6 Conclusions and plans
This was the third year of the Social Book Search Track. This year, the track
ran three tasks: the Social Book Search task, the Prove It task and the Structure
Extraction task.
RunID Participant F-measure
MDCS MDCS 43.61%
Nankai Nankai U. 35.41%
Innsbruck Innsbruck U. 31.34%
Würzburg Würzburg U. 19.61%
Epita Epita 14.96%
GREYC-run-d University of Caen 8.81%
GREYC-run-c University of Caen 7.91%
GREYC-run-a University of Caen 6.21%
GREYC-run-e University of Caen 4.71%
GREYC-run-b University of Caen 3.79%
Table 11. Summary of title-based performance scores for the Structure Extrac-
tion competition 2013 (F-measure for complete entries).
The Social Book Search (SBS) task continued which its focus on both the
relative value of professional and user-generated metadata and the retrieval and
recommendation aspects of the LT forum users and their information needs. The
number of active participants has doubled from 4 to 8, suggesting a promising
future for the task.
Expanding on the evaluation of the previous two years, we delved deeper into
the nature of book search information needs and book suggestions from the LT
forums. We annotated both 1) the information needs described by the starters of
topic thread there were asking for book suggestions, and 2) the books suggested
by LT members in the thread.
We found that most social book search topics have requirements related to
the content of the book, such as topic and genre, but that metadata, familiarity
and engagement—asking for books by a certain author, books that are similar
to a particular (set of) book(s) and books that fit a certain mood, interest or
quality respectively—are also important aspects. This strengthens and extends
our findings from last year that social book search topics express complex needs
that are hard to satisfy with current book search services, but also to specific
for typical recommendation systems.
Another finding in the SBS task is that forum members mostly suggest books
they have read although there are also many cases where it is hard to judge from
what they write about the books they suggest. When it is clear they have read
the books they suggest read, they are mostly positive, supporting our choice of
using forum suggestions as relevance judgements. When they suggest books they
have not read, or when it is hard to tell, their are often neutral. This could be a
signal that suggestions of unread books are closer to traditional topical relevance
judgements and suggestions of read books are topic specific recommendations
that satisfy all or most of the complex combination of relevance aspects.
The evaluation has shown that the most effective systems incorporate the full
topic statement, which includes the title of the topic thread, a query provided
by the annotator, the name of the forum discussion group, and the full first
message that elaborates on the request. However, the best system is a plain full-
text retrieval system that ignores all user profile information. It could be that
the suggestions by members other than the topic starter favour non-personalised
retrieval models and thereby muddle the personalised signal of suggestions sup-
ported by the topic starter.
Next year, we plan to shift the focus of the SBS task to the interactive nature
of the topic thread and the suggestions and responses given by the topic starter
and other members. We are also thinking of a pilot task in which the system not
only has to retrieve relevant and recommendable books, but also to select which
part of the book description—e.g. a certain set of reviews or tags—is most useful
to show to the user, given her information need.
This year the Prove It task changed somewhat by adding the criterium that
returned pages should come from reliable sources. That is, authoritative books
of the appropriate genre. The assessment phase is yet to start so there are no
evaluation results yet. Due to the low number of participants over the last years,
the task will probably not run again next year.
The SE task relies on a subset of the 50,000 digitised books of the PI task.
In 2013, the participants were to extract the tables of contents of 1,000 books
extracted from the whole PI book collection. In previous years, the ground truth
was constructed collaboratively by participating institution. For the first, time in
2013 the ground truth production was performed by an external provider. This
centralised construction granted better consistency. In addition, it also validated
the collaborative process used since 2009, as the results this year were in line
with those of the previous rounds.
The structure extraction task has reached a record high number of active
participants, and has for the first time witnessed an improvement of the state of
the art. In future years, we aim to investigate the usability of the extracted ToCs,
both for readers in navigating books and systems that index and search parts of
books. To be able to build even larger evaluation sets, we hope to experiment
with crowdsourcing methods. This may offer a natural solution to the evaluation
challenge posed by the massive data sets handled in digitised libraries.
Acknowledgments We are very grateful to Toine Bogers for helping us with the
topic annotation tool and for recruiting LIS students to be annotators for this
year’s topic selection and relevance assessments.
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