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https://ceur-ws.org/Vol-1610/editorial.pdf
BIRNDL 2016 Joint Workshop on Bibliometric-enhanced Information Retrieval and NLP for Digital Libraries
Editorial for the Joint Workshop on
Bibliometric-enhanced Information Retrieval and
Natural Language Processing for Digital Libraries
(BIRNDL) at JCDL 2016
Philipp Mayr1 , Ingo Frommholz2 , Guillaume Cabanac3 , and Dietmar Wolfram4
1
GESIS - Leibniz-Institute for the Social Sciences, Cologne, Germany,
philipp.mayr@gesis.org
2
Institute for Research in Applicable Computing, University of Bedfordshire,
Luton, UK,
ingo.frommholz@beds.ac.uk
3
University of Toulouse, Computer Science Department, IRIT UMR 5505, France
guillaume.cabanac@univ-tlse3.fr
4
School of Information Studies, University of Wisconsin-Milwaukee, USA
dwolfram@uwm.edu
1 Introduction
After the success of two parent workshops series – the 1st NLPIR4DL work-
shop in 2009, and the series of three Bibliometric-enhanced Information Re-
trieval (BIR) workshops in 2014, 2015 and 2016 – BIRNDL5 at JCDL 2016
[1] will investigate how natural language processing, information retrieval, sci-
entometric and recommendation techniques can advance the state-of-the-art in
scholarly document understanding, analysis and retrieval at scale. Researchers
are in need of assistive technologies to track developments in an area, identify
the approaches used to solve a research problem over time and summarize re-
search trends. Digital libraries require semantic search, question-answering as
well as automated recommendation and reviewing systems to manage and re-
trieve answers from scholarly databases. Full document text analysis can help
to design semantic search, translation and summarization systems; citation and
social network analyses can help digital libraries to visualize scientific trends,
bibliometrics and relationships and influences of works and authors. These ap-
proaches can be supplemented with the metadata supplied by digital libraries,
such as usage data.
This workshop will be relevant to scholars in several fields of computer sci-
ence, information science and computational linguistics; it will also be of impor-
tance for all stakeholders in the publication pipeline: implementers, publishers
and policymakers – with this workshop we hope to bring a number of these con-
tributors together. Today’s publishers continue to seek new ways to be relevant
to their consumers, in disseminating the right published works to their audience.
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BIRNDL 2016 Joint Workshop on Bibliometric-enhanced Information Retrieval and NLP for Digital Libraries
Formal citation metrics are increasingly a factor in decision-making by universi-
ties and funding bodies worldwide, making the need for research in such topics
more pressing.
The BIRNDL event was split into two parts: the regular research paper track
and the CL-SciSumm Shared Task system track.
2 Overview of the papers
The workshop featured one keynote talk, three paper sessions and one poster
and demo interactive session. The BIRNDL organizers have accepted 5 long and
4 short papers for presentation in the research paper track. The CL-SciSumm
organizers have accepted 9 system papers in the CL-SciSumm track. All papers
in both tracks are included in the proceedings. The following briefly outlines the
keynote and three paper sessions. The system papers in the CL-SciSumm track
are outline in an overview paper [2].
2.1 Keynote
Dietmar Wolfram provided the keynote address on “Bibliometrics, Information
Retrieval and Natural Language Processing: Natural Synergies to Support Dig-
ital Library Research”[3]. Until recently, methods developed for IR and biblio-
metrics that can be mutually beneficial have not been widely explored. This
is changing as evidenced by recent themed meetings that have brought to-
gether researchers with interests that bridge both areas. Similarly, applications
of language-based methods have provided new tools for research in bibliomet-
rics and IR. The presenter discussed examples of the synergies that exist at the
intersections of these three areas, not only for IR system design and evaluation,
but also to provide insights into the structure of disciplines and their research
communities.
2.2 Session 1
In their article “Multiple In-text Reference Phenomenon”, Bertin and Atanassova
studied the distribution of multiple in-text references (MIR), which are based
on sentences with more than one reference [4]. A corpus of 80,000 PLOS papers
was used for the analysis and references were counted based on the publications’
IMRaD structure. The results revealed, for instance, that 41% of sentences with
citations contain MIRs, with more than half of them in the introduction. Poten-
tial applications of this study comprised works on clustering, co-citation networks
and summarization.
Citations to retracted paper were the focus of the contribution “Post Re-
traction Citations in Context” by Halevi and Bar-Ilan [5]. Citations to retracted
articles might put the credibility of scientific work in jeopardy, hence it is a field
worth studying. The authors discuss 5 case studies of retracted papers and the
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BIRNDL 2016 Joint Workshop on Bibliometric-enhanced Information Retrieval and NLP for Digital Libraries
negative, positive and neutral citations they received after retraction. The au-
thors expressed their concern about the fact that retracted articles still attract
citations, and provide some recommendation for publishers.
In his paper “Incorporating Satellite Documents into Co-citation Networks
for Scientific Paper Searches”, Masaki Eto examined the use of enlarged co-
citation networks to improve IR search performance for documents from the
Open Access Subset of PubMed Central [6]. Satellite documents to expand the
network of linkages beyond direct co-citations were identified based on search
terms appearing in documents co-cited with a seed document. Results of the
study revealed that the proposed method provided better search performance
than a baseline approach that did not incorporate the enlarged network.
To master the huge amount of scientific literature produced nowadays and
make sense of the rich pool of knowledge they provide, Ronzano et al. introduced
the Scientific Knowledge Miner project [7]. Based on a previous text mining
project, SKM aims at extending the existing Dr. Inventor Scientific Text Mining
Framework, and offers services like summarization and citation recommendation.
2.3 Session 2
Ha Jin Kim, Juyoung An, Yoo Kyung Jeong and Min Song presented the results
of their research on “Exploring the Leading Authors and Journals in Major
Topics by Citation Sentences and Topic Modeling” [8]. The authors employed an
Author-Journal-Topic (AJT) model to identify leading journals and authors in
the area of Oncology along with major topics that are shared among researchers.
A key finding was that influential authors and journals identified using topic
modeling did not necessarily correspond to those identified using citation-based
measures. The authors concluded that the AJT model may be used to identify
latent meaning in citation sentences.
Aravind Sesagiri Raamkumar, Schubert Foo, and Natalie Pang tackled a
compelling question every scientist wonders while writing: “What papers should
I cite from my reading list? User evaluation of a manuscript preparatory assis-
tive task” [9]. They introduced techniques for shortlisting papers from a personal
bibliography and discussed their effectiveness based on user evaluations. A panel
of 116 users — balanced between students and staff members — rated the rec-
ommendations according to a variety of criteria, such as relevance, usefulness,
importance, and certainty. Their positive feedback stresses the usefulness and
relevance of this paper recommendation contribution.
2.4 Session 3
Jevin West and Jason Portenoy focusedon a largely ignored facet of scholarly
papers – the equations [10], in their paper, “Delineating Fields Using Mathemat-
ical Jargon”. They extracted mathematical symbols from Latex source files in
the arXiv repository, performed an analysis of the distribution of these symbols
across different fields and calculated the “jargon distance” between fields. The
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BIRNDL 2016 Joint Workshop on Bibliometric-enhanced Information Retrieval and NLP for Digital Libraries
main research goal of their paper was to find ways to utilize equations and formal
notation in scholarly recommendation.
Joseph Mariani, Gil Francopoulo, and Patrick Paroubek discussed “A study
of reuse and plagiarism in speech and natural language processing papers” [11].
They designed an algorithm based on n-gram comparisons to detect (self-)reuse
and (self-)plagiarism. It was tested on the NLP4NLP dataset comprising about
65k NLP papers published during the past five decades. Results stress frequent
self-plagiarism while uncommon plagiarism in the scientific literature of NLP.
Philipp Mayr presented a case study “How do practitioners, PhD students and
postdocs in the social sciences assess topic-specific recommendations?” where dif-
ferent types of researchers in the social sciences assessed the relevance of search
term, author name and journal name recommendations according to their re-
search topics [12]. His results showed that simple bibliometric-enhanced recom-
mendation services can be useful where they are integrated in an interactive
retrieval task.
2.5 CL-SciSumm Shared Task
As part of this workshop, our colleagues at the National University of Singapore
organized the CL-SciSumm Shared Task 20166 – a shared task on scientific
paper summarization in the Computational Linguistics domain. This proceedings
includes an outline of their Shared Task, as well as detailed system reports from
the ten participating systems who completed the Task [2].
3 Outlook
This workshop is the first step to foster a reflection on the interdisciplinarity
and the benefits that the disciplines Bibliometrics, IR and NLP can drive from
it in a digital libraries context. In the future we plan follow-up workshops at IR,
NLP and Digital Libraries venues. Furthermore we are working with the Inter-
national Journal on Digital Libraries to offer a special issue on topics discussed
at BIRNDL, for extended versions of BIRNDL workshop papers, shared task
descriptions, as well as a general call for submissions.7
4 Acknowledgments
We are indebted to the referees who contributed to the review process: Colin
Batchelor, Joeran Beel, Patrice Bellot, Marc Bertin, Guillaume Cabanac, Cor-
nelia Caragea, Zeljko Carevic, Muthu Kumar Chandrasekaran, Jason S. Chang,
Ingo Frommholz, Lee Giles, Bela Gipp, Daniel Hienert, Rahul Jha, Min-Yen
Kan, Noriko Kando, Roman Kern, Claus-Peter Klas, Cyril Labbé, Birger Larsen,
Elizabeth Liddy, Stasa Milojevic, Prasenjit Mitra, Marie-Francine Moens, Peter
Mutschke, Doug Oard, Cécile Paris, Philipp Schaer, Andrea Scharnhorst, Henry
Small, Simone Teufel, Mike Thelwall, Alex Wade, and Dietmar Wolfram.
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7
See information at http://wing.comp.nus.edu.sg/birndl-jcdl2016.
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BIRNDL 2016 Joint Workshop on Bibliometric-enhanced Information Retrieval and NLP for Digital Libraries
References
1. Cabanac, G., Chandrasekaran, M.K., Frommholz, I., Jaidka, K., Kan, M.Y., Mayr,
P., Wolfram, D.: Joint Workshop on Bibliometric-enhanced Information Retrieval
and Natural Language Processing for Digital Libraries (BIRNDL 2016). In: JCDL
’16: Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Li-
braries, ACM New York, NY, USA (2016) 299–300
2. Jaidka, K., Chandrasekaran, M.K., Rustagi, S., Kan, M.Y.: Overview of the CL-
SciSumm 2016 Shared Task. In: Proc. of the Joint Workshop on Bibliometric-
enhanced Information Retrieval and Natural Language Processing for Digital Li-
braries (BIRNDL2016). (2016)
3. Wolfram, D.: Bibliometrics, Information Retrieval and Natural Language Process-
ing: Natural Synergies to Support Digital Library Research. In: Proc. of the Joint
Workshop on Bibliometric-enhanced Information Retrieval and Natural Language
Processing for Digital Libraries (BIRNDL2016). (2016) 6–13
4. Bertin, M., Atanassova, I.: Multiple In-text Reference Aggregation Phenomenon.
In: Proc. of the Joint Workshop on Bibliometric-enhanced Information Retrieval
and Natural Language Processing for Digital Libraries (BIRNDL2016). (2016) 14–
22
5. Halevi, G., Bar-Ilan, J.: Post Retraction Citations in Context. In: Proc. of the Joint
Workshop on Bibliometric-enhanced Information Retrieval and Natural Language
Processing for Digital Libraries (BIRNDL2016). (2016) 23–29
6. Eto, M.: Incorporating Satellite Documents into Co-citation Networks for Scien-
tific Paper Searches. In: Proc. of the Joint Workshop on Bibliometric-enhanced
Information Retrieval and Natural Language Processing for Digital Libraries
(BIRNDL2016). (2016) 30–35
7. Ronzano, F., Freire, A., Saez-Trumper, D., Saggion, H.: Making Sense of Massive
Amounts of Scientific Publications: the Scientific Knowledge Miner Project. In:
Proc. of the Joint Workshop on Bibliometric-enhanced Information Retrieval and
Natural Language Processing for Digital Libraries (BIRNDL2016). (2016) 36–41
8. Kim, H.J., An, J., Jeong, Y.K., Song, M.: Exploring the leading authors and
journals in major topics by citation sentences and topic modeling. In: Proc. of
the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural
Language Processing for Digital Libraries (BIRNDL2016). (2016) 42–50
9. Raamkumar, A.S., Foo, S., Pang, N.: What papers should I cite from my read-
ing list? User evaluation of a manuscript preparatory assistive task. In: Proc. of
the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural
Language Processing for Digital Libraries (BIRNDL2016). (2016) 51–62
10. West, J., Portenoy, J.: Delineating Fields Using Mathematical Jargon. In: Proc. of
the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural
Language Processing for Digital Libraries (BIRNDL2016). (2016) 63–71
11. Mariani, J., Francopoulo, G., Paroubek, P.: A study of reuse and plagiarism in
speech and natural language processing papers. In: Proc. of the Joint Workshop
on Bibliometric-enhanced Information Retrieval and Natural Language Processing
for Digital Libraries (BIRNDL2016). (2016) 72–83
12. Mayr, P.: How do practitioners, PhD students and postdocs in the social sci-
ences assess topic-specific recommendations? In: Proc. of the Joint Workshop on
Bibliometric-enhanced Information Retrieval and Natural Language Processing for
Digital Libraries (BIRNDL2016). (2016) 84–92
5