=Paper=
{{Paper
|id=None
|storemode=property
|title=
Experimenting Text Summarization Techniques for Contextual Advertising
|pdfUrl=https://ceur-ws.org/Vol-704/12.pdf
|volume=Vol-704
|dblpUrl=https://dblp.org/rec/conf/iir/ArmanoGV11
}}
==
Experimenting Text Summarization Techniques for Contextual Advertising
==
https://ceur-ws.org/Vol-704/12.pdf
Experimenting Text Summarization Techniques
for Contextual Advertising
Giuliano Armano, Alessandro Giulian, and Eloisa Vargiu
University of Cagliari
Department of Electrical and Electronic Engineering
{armano, alessandro.giuliani, vargiu}@diee.unica.it
http://iasc.diee.unica.it
Abstract. Contextual advertising systems suggest suitable advertisings
to users while surfing the Web. Focusing on text summarization, we pro-
pose novel techniques for contextual advertising. Comparative experi-
ments between these techniques and existing ones have been performed.
Keywords: contextual advertising, information retrieval and filtering
1 Introduction
Most of the advertisements on the Web are short textual messages, usually
marked as “sponsored links”. Two main kinds of textual advertising approaches
are used on the Web today [8]: sponsored search and contextual advertising. The
former puts advertisements (ads) on the pages returned from a Web search en-
gine following a query. All major current Web search engines support this kind
of ads, acting simultaneously as search engine and advertisement agency. The
latter puts ads within the content of a generic, third party, Web page. A com-
mercial intermediary, namely an ad-network, is usually in charge of optimizing
the selection of ads. In other words, contextual advertising (CA hereinafter) is
a form of targeted advertising for ads appearing on websites or other media,
such as contents displayed in mobile browsers. Ads are selected and served by
automated systems based on the content displayed to the user.
We consider a scenario of online advertising, in which an intermediating com-
mercial net (ad-network) is responsible for optimizing the selection of ads. The
goal is twofold: (i) increasing commercial company revenues and (ii) improving
user experience. Let us point out in advance that, in information retrieval, the
term “context” may have different interpretations depending on the research
field. For instance, it denotes “event which modify the user behavior in the field
of recommender systems”. For CA it denotes “keywords used in search engines”.
A CA system typically involves four main tasks: (i) pre-processing, (ii) text
summarization, (iii) classification, and (iv) matching. In this paper, we are
mainly interested in text summarization, which is aimed at generating a short
representation of a textual document (e.g., a Web page) with negligible loss of
information.
2 Armano, Giuliani and Vargiu
Starting from state-of-the-art text-summarization techniques, we propose
new and more effective techniques. Then, we perform comparative experiments
to assess the effectiveness of the proposed techniques. Preliminary results show
that the proposed techniques perform better than existing ones.
The paper is organized as follows. First, the main work on CA is briefly
recalled. Subsequently, text summarization is illustrated from both a generic
perspective and in the context of CA. After illustrating an implementation of a
CA system, preliminary experimental results are then reported and discussed.
Conclusions and future directions end the paper.
2 Contextual Advertising
As discussed in [6], CA is an interplay of four players:
– The advertiser provides the supply of ads. Usually the activity of the adver-
tisers is organized around campaigns which are defined by a set of ads with
a particular temporal and thematic goal (e.g., sale of digital cameras during
the holiday season). As in traditional advertising, the goal of the advertisers
can be broadly defined as the promotion of products or services.
– The publisher is the owner of the Web pages on which the advertising is dis-
played. The publisher typically aims to maximize advertising revenue while
providing a good user experience.
– The ad network is a mediator between the advertiser and the publisher;
it selects the ads to display on the Web pages. The ad-network shares the
advertisement revenue with the publisher.
– The Users visit the Web pages of the publisher and interact with the ads.
Ribeiro-Neto et al. [22] examine a number of strategies to match pages and
ads based on extracted keywords. Ads and pages are represented as vectors in
a vector space. To deal with semantic problems that may arise from a pure
keyword-based approach, the authors expand the page vocabulary with terms
from similar pages weighted according to their similarity to the matched page.
In a subsequent work, the authors propose a method to learn the impact of
individual features using genetic programming [16].
Another approach to CA is to reduce it to the problem of sponsored search
by extracting phrases from a Web page and matching them with the bid phrases
of each ad. In [26], a system for phrase extraction is proposed, which uses a
variety of features to determine the importance of page phrases for advertising
purposes. The system is trained with pages that have been annotated by hand
with important phrases. In [6], the same approach is used, with a phrase extractor
based on the work reported in [25].
3 Text Summarization
Radev et al. [21] define a summary as “a text that is produced from one or
more texts, that conveys important information in the original text(s), and that
Experimenting Text Summarization... 3
is no longer than half of the original text(s) and usually significantly less than
that”. This simple definition highlights three important aspects that character-
ize research on automatic summarization: (i) summaries may be produced from
a single document or multiple documents; (ii) summaries should preserve impor-
tant information; and (iii) summaries should be short. Unfortunately, attempts
to provide a more elaborate definition for this task resulted in disagreement
within the community [7].
Summarization techniques can be divided into two groups [15]: (i) those that
extract information from the source documents (extraction-based approaches)
and (ii) those that abstract from the source documents (abstraction-based ap-
proaches). The former impose the constraint that a summary uses only com-
ponents extracted from the source document, whereas the latter relax the con-
straints on how the summary is created. Extraction-based approaches are mainly
concerned with what the summary content should be, usually relying solely on
extraction of sentences. On the other hand, abstraction-based approaches put
strong emphasis on the form, aiming to produce a grammatical summary, which
usually requires advanced language generation techniques. Although potentially
more powerful, abstraction-based approaches have been far less popular than
their extraction-based counterparts, mainly because generating the latter is eas-
ier. In a paradigm more tuned to information retrieval, one can also consider
topic-driven summarization, which assumes that the summary content depends
on the preference of the user and can be assessed via a query, making the final
summary focused on a particular topic. In this paper, we exclusively focus on
extraction-based methods.
An extraction-based summary consists of a subset of words from the original
document and its bag of words representation can be created by selectively
removing a number of features from the original term set. In text categorization,
such process is known as feature selection and is guided by the “usefulness” of
individual features as far as the classification accuracy is concerned. However, in
the context of text summarization, feature selection is only a secondary aspect.
It might be argued that in some cases a summary may contain the same set
of features as the original; for example, when it is created by removing the
redundant/repetitive words or phrases. Typically, an extraction-based summary
whose length is only 10-15% of the original is likely to lead to a significant feature
reduction as well.
Many studies suggest that even simple summaries are quite effective in car-
rying over the relevant information about a document. From the text categoriza-
tion perspective, their advantage over specialized feature selection methods lies
in their reliance on a single document only (the one that is being summarized)
without computing the statistics for all documents sharing the same category
label, or even for all documents in a collection. Moreover, various forms of sum-
maries become ubiquitous on the Web and in certain cases their accessibility
may grow faster than that of full documents.
Earliest instances of research on summarizing scientific documents proposed
paradigms for extracting salient sentences from text using features like word and
4 Armano, Giuliani and Vargiu
phrase frequency [17], position in the text [3], and key phrases [10]. Various works
published since then had concentrated on other domains, mostly on newswire
data. Many approaches addressed the problem by building systems dependent
on the type of the required summary.
Simple summarization-like techniques have been long applied to enrich the
set of features used in text categorization. For example, a common strategy is to
give extra weight to words appearing in the title of a story [19] or to treat the
title-words as separate features, even if the same words were present elsewhere in
the text body [9]. It has been also noticed that many documents contain useful
formatting information, loosely defined as context, that can be utilized when
selecting the salient words, phrases or sentences. For example, Web search en-
gines select terms differently according to their HTML markup [4]. Summaries,
rather than full documents, have been successfully applied to document cluster-
ing [11]. Ker and Chen [13] evaluated the performance of a categorization system
using title-based summaries as document descriptors. In their experiments with
a probabilistic TF-IDF based classifier, they shown that title-based document
descriptors positively affected the performance of categorization.
4 Text Summarization in Contextual Advertising
As the input of a contextual advertiser is an HTML document, contextual adver-
tising systems typically rely on extraction-based approaches, which are applied
to the relevant blocks of a Web page (e.g., the title of the Web page, its first
paragraph, and the paragraph which has the highest title-word count).
In the work of Kolcz et al. [15] seven straightforward (but effective) extraction-
based text summarization techniques have been proposed and compared. In all
cases, a word occurring at least three times in the body of a document is a
keyword, while a word occurring at least once in the title of a document is a
title-word. For the sake of completeness, let us recall the proposed techniques:
– Title (T), the title of a document;
– First Paragraph (FP), the first paragraph of a document;
– First Two Paragraphs (F2P), the first two paragraphs of a document;
– First and Last Paragraphs (FLP), the first and the last paragraphs of a
document;
– Paragraph with most keywords (MK), the paragraph that has the highest
number of keywords;
– Paragraph with most title-words (MT), the paragraph that has the highest
number of title-words;
– Best Sentence (BS), sentences in the document that contain at least 3 title-
words and at least 4 keywords.
One may argue that the above methods are too simple. However, as shown
in [5], extraction-based summaries of news articles can be more informative
than those resulting from more complex approaches. Also, headline-based article
descriptors proved to be effective in determining user’s interests [14].
Experimenting Text Summarization... 5
Our proposal consists of enriching some of the techniques introduced by Kolcz
et al. with information extracted from the title, as follows:
– Title and First Paragraph (TFP), the title of a document and its first para-
graph:
– Title and First Two Paragraphs (TF2P), the title of a document and its first
two paragraphs;
– Title, First and Last Paragraphs (TFLP), the title of a document and its
first and last paragraphs;
– Most Title-words and Keywords (MTK), the paragraph with the highest
number of title-words and that with the highest number of keywords.
We also defined a further technique, called NKeywords (NK), that selects the
N most frequent keywords.1
(a)
(b)
Fig. 1. A generic CA architecture at a glance.
1
N is a global parameter that can be set starting from some relevant characteristics
of the input (e.g., from the average document length).
6 Armano, Giuliani and Vargiu
5 The Implemented System
Our view of CA is sketched in Figure 1, which illustrates a generic architecture
that can give rise to specific systems depending on the choices made on each
involved module. Notably, most of the state-of-the-art solutions are compliant
with this view. So far, we implemented in Java the sub-system depicted in Figure
1.a, which encompasses (i) a pre-processor, (ii) a text summarizer, and (iii) a
classifier.
Pre-processor. Its main purpose is to transform an HTML document (a Web
page or an ad) into an easy-to-process document in plain-text format, while
maintaining important information. This is obtained by preserving the blocks
of the original HTML document, while removing HTML tags and stop-words.2
First, any given HTML page is parsed to identify and remove noisy elements,
such as tags, comments and other non-textual items. Then, stop-words are re-
moved from each textual excerpt. Finally, the document is tokenized and each
term stemmed using the well-known Porter’s algorithm [20].
Text summarizer. The text summarizer outputs a vector representation of the
original HTML document as bag of words (BoW), each word being weighted by
TF-IDF [23]. So far, we implemented the methods of Kolcz et al. (see Section
4), but not “Title” and “Best Sentence”. These two methods were defined to
extract summaries from textual documents such as articles, scientific papers
and books. In fact, we are interested in summarizing HTML documents, in which
the title is often not representative. Moreover, they are often too short to find
meaningful sentences composed by at least 3 title-words and 4 keywords in the
same sentence.
Classifier. Text summarization is a purely syntactic analysis and the correspond-
ing Web-page classification is usually inaccurate. To alleviate possible harmful
effects of summarization, both page excerpts and advertisings are classified ac-
cording to a given set of categories [2]. The corresponding classification-based
features (CF) are then used in conjunction with the original BoW. In the current
implementation, we adopt a centroid-based classification technique [12], which
represents each class with its centroid calculated starting from the training set.
A page is classified measuring the distance between its vector and the centroid
vector of each class by adopting the cosine similarity.
Matcher. It is devoted to suggest ads (a) to the Web page (p) according to
a similarity score based on both BoW and CF [2]. In formula (α is a global
parameter that permits to control the emphasis of the syntactic component with
respect to the semantic one):
score(p, a) = α · simBoW (p, a) + (1 − α) · simCF (p, a) (1)
2
To this end, the Jericho API for Java has been adopted, described at the Web page:
http://jericho.htmlparser.net/docs/index.html
Experimenting Text Summarization... 7
where, simBoW (p, a) and simCF (p, a) are cosine similarity scores between p and
a using BoW and CF, respectively. This module has not been implemented yet.
However, it is worth recalling that in this paper we are interested in making
comparisons among text summarization techniques.
6 Preliminary Results
We performed experiments aimed at comparing the techniques described in Sec-
tion 4. To assess them we used the BankSearch Dataset [24], built using the
Open Directory Project and Yahoo! Categories3 , consisting of about 11000 Web
pages classified by hand in 11 different classes.
Fig. 2. Class hierarchy of BankSearch Dataset.
Figure 2 shows the overall hierarchy. The 11 selected classes are the leaves
of the taxonomy, together with the class Sport, which contains web documents
from all the sites that were classified as sport, except for the sites that were
classified as Soccer or Motor Sport. In [24], the authors show that this structure
provides a good test not only for generic classification/clustering methods, but
also for hierarchical techniques.
Table 1 shows the performances in terms of accuracy (A), macro-precision
(P), and macro-recall (R). For each technique, the average number of unique
3
http://www.dmoz.org and http://www.yahoo.com, respectively
8 Armano, Giuliani and Vargiu
Table 1. Results of text summarization techniques comparison.
FP F2P FLP MK MT TFP TF2P TFLP MTK NK
A 0.598 0.694 0.743 0.608 0.581 0.802 0.821 0.833 0.721 0.715
P 0.606 0.699 0.745 0.702 0.717 0.802 0.822 0.832 0.766 0.722
R 0.581 0.673 0.719 0.587 0.568 0.772 0.789 0.801 0.699 0.693
T 13 24 24 25 15 16 27 26 34 10
extracted terms (T) is shown. For NKeywords summarization, we performed
experiments with N=10.
As a final remark, let us note that just adding information about the title
improves the performances of summarization. Another interesting result is that,
as expected, the TFLP summarization provides the best performance, as FLP
summarization does for the classic techniques.
7 Conclusions and Future Directions
In this paper, we presented a preliminary study on text summarization tech-
niques applied to CA. In particular, we proposed some straightforward extraction-
based techniques that improve those proposed in the literature. Experimental
results confirm the hypothesis that adding information about titles to well-known
techniques allows to improve performances.
As for future directions, we are currently studying a novel semantic technique.
The main idea is to improve syntactic techniques by exploiting semantic informa-
tion (such as, synonyms and hypernyms) extracted from a lexical database (e.g.,
WordNet [18]) in conjunction with a POS-tagging and word sense disambigua-
tion. Further experiments are also under way. In particular, we are setting up the
system to calculate its performances with a larger dataset extracted by DMOZ
in which documents are categorized according to a given taxonomy of classes.
Moreover, as we deem that bringing ideas from recommender systems will help
in devising CA systems [1], we are also studying a collaborative approach to CA.
Acknowledgments. This work has been partially supported by Hoplo srl. We
wish to thank, in particular, Ferdinando Licheri and Roberto Murgia for their
help and useful suggestions.
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