Call for Participation
You are invited to participate in the CL-SciSumm Shared Task at
BIRNDL 2017. The shared task will be on automatic paper summarization
in the Computational Linguistics (CL) domain.
This year also we will invite the authors of selected system papers
at the CL-SciSumm Shared Task, to submit extended versions to a special
issue in International Journal of Digital Libraries (IJDL). The special issue
will revolve on themes associated with the BIRNDL workshop.
This task follows up on the successful
CLScisumm-16 at the BIRNDL
workshop co-located with JCDL 2016, Rutgers, NJ, USA and
the CL Pilot Task conducted as a part of
the BiomedSumm Track
at the Text Analysis Conference 2014 (TAC 2014).
Over the two editions, a
training corpus of ten topics and a development corpus of ten more topics
from CL research papers were released. Participants were invited to
enter their systems in a task-based evaluation. We also released the
annotated dataset, comprising of ACL Computational Linguistics
research papers and summaries.
The output summaries are of two types: faceted summaries of the
traditional self-summary (the abstract) and the community summary
(the collection of citation sentences ‘citances’). We also
group the citances by the facets of the text that they refer to.
In our proposed shared task, we will expand the corpus with a
new test dataset of 10 topics (closed for evaluation).
The CL-SciSumm 2017 corpus is expected to be of interest to a broad
community including those working in computational linguistics and
natural language processing, text summarization, discourse structure
in scholarly discourse, paraphrase, textual entailment and text
We have secured support for the costs of the shared task annotation
Research Asia. The National University of Singapore will be
primarily responsible for the task's oversight.
Given: A topic consisting of a Reference Paper (RP) and Citing
Papers (CPs) that all contain citations to the RP. In each CP, the text
spans (i.e., citances) have been identified that pertain to a particular
citation to the RP.
Task 1A: For each citance, identify the spans of text (cited text
spans) in the RP that most accurately reflect the citance. These are
of the granularity of a sentence fragment, a full sentence, or several
consecutive sentences (no more than 5).
Task 1B: For each cited text span, identify what facet of the
paper it belongs to, from a predefined set of facets.
Task 2 (optional bonus task): Finally, generate a structured
summary of the RP from the cited text spans of the RP. The length of the
summary should not exceed 250 words.
Evaluation: Task 1 will be scored by overlap of text spans
measured by number of sentences in the system output vs the gold standard
created by human annotators. Task 2 will be scored using the ROUGE family
of metrics between (i) the system output and the gold standard summary from
the reference spans (ii) the system output and the asbtract of the
Organizations wishing to participate in the CL Shared Task track
at BIRNDL 2017 are invited to register on EasyChair
by May 31st. Participants are advised to register as soon as
possible in order to receive timely access to evaluation resources,
including development and testing data. Registration for the task
does not commit you to participation - but is helpful to know for
planning. All participants who submit system runs are welcome to
present their system at the BIRNDL Workshop.
Dissemination of CL-SciSumm work and results other than in the
workshop proceedings is welcomed, but the conditions of participation
specifically preclude any advertising claims based on these
results. Any questions about conference participation may be sent
to the organizers mentioned below.
The corpus for the CL-SciSumm task has been created by randomly
sampling ten documents from the ACL Anthology corpus and selecting
their citing papers. The training, development and testing set will
be made publicly available at the GitHub link above at dates
The training set of 10 articles is already available for
and can be used by participants to pilot their systems. The
development set of 10 articles, a part of the same corpus, will be
released in April, which participants can add to the training set to
tune their system parameters. Finally the test set of 10 articles
will be released in July 1st. The system outputs from the test set should
be submitted to the task organizers, for the collation of the final
results to be presented at the workshop.
The best performing system
on Task - 1a, the core component of Shared Task this
year is from NJUST. Here is a recording of their talk
Please consult the BIRNDL
Workshop for official dates for the workshop.
|Training Set Release||May 1|
|Deadline for Registration and Short System Descriptions Due|May 20 May 31 June 4
|Test Set Released||July 1|
|System Runs Due|July 15 July 22
|Preliminary System Reports Due in EasyChair||July 30|
|Camera-Ready Contributions Due in EasyChair||TBD|
|Participant Presentations at BIRNDL Workshop||August 11 in Tokyo, Japan|
for the CL-SciSumm shared task
are calculated as 11:59pm Baker Island Time (BIT: UTC/GMT-12).
The CL-SciSumm task provides resources to encourage research in a
promising direction of scientific paper summarization, which considers
the set of citation sentences (i.e., "citances") that reference a
specific paper as a (community created) summary of a topic or paper
(Nanba, Kando and Okumura, 2011; Qazvinian and Radev, 2008). Citances
for a reference paper are considered a synopses of its key points and
also its key contributions and importance within an academic community.
The advantage of using citances is that they are embedded with
meta-commentary and offer a contextual, interpretative layer to the
cited text. The drawback, however, is that though a collection of
citances offers a view of the cited paper, it does not consider the
context of the target user (Sparck Jones, 2007; Teufel and Moens, 2002;
Nenkova and McKeown, 2011; Jaidka, Khoo and Na, 2013a), verify the claim
of the citation or provide context from the reference paper, in terms
the type of information cited or where it is in the referenced paper.
CL-SciSumm explores summarization of scientific research, for the
computational linguistics research domain. An ideal summary of
computational linguistics research papers would be able to summarize
previous research by drawing comparisons and contrasts between their
goals, methods and results, as well as distil the overall trends in the
state of the art and their place in the larger academic discourse.
Literature surveys and review articles in CL do help readers to gain a
gist of the state-of-the-art in research for a topic. However,
literature survey writing is labor-intensive and a literature survey is
not always available for every topic of interest. What are needed, are
resources which automate the synthesis and updating of automatic
summaries of CL research papers.
Existing scientific summarization systems have automatically
generated related work sections for a target paper by instantiating a
hierarchical topic tree (Hoang and Kan, 2010), generating model citation
sentences (Mohammad et al., 2009) or implementing a literature review
framework (Jaidka et al., 2013). However, the limited availability of
evaluation resources and human-created summaries constrains research in
this area. The goal of the TAC 2014 CL Shared Task is to highlight the
challenges and relevance of the scientific summarization problem,
support research in automatic scientific document summarization and
provide evaluation resources to push the current state of the art.
BIRNDL 2017 Workshop
The BIRNDL 2017 workshop will be held in Tokyo,
, Japan. The workshop is a forum both for presentation of results
(including failure analyses and system comparisons), and for more
lengthy system presentations describing techniques used, experiments
run on the data, and other issues of interest to NLP researchers. TAC
track participants who wish to give a presentation during the workshop
will submit a short abstract describing the experiments they
performed. As there is a limited amount of time for oral
presentations, the abstracts will be used to determine which
participants are asked to speak and which will present in a poster
- Muthu Kumar Chandrasekaran - firstname.lastname@example.org
He is broadly interested in natural language processing and
its applications to information retrieval; specifically, in retrieving
and organising information from asynchronous conversation media such
as scholarly publications, discussion and debate forums. He was on
the organizing committee of the CL-SciSumm 2016 Shared Task, the
CL-SciSumm 2014 Pilot Task and the BIRNDL workshop. He also reviews
for BIR, ACL and EMNLP conferences. He believes communication of
scholarly research needs to be summarized to avoid redundant or
outdated research and ensure faster progress to pressing problems.
He is currently doing his Ph.D. research on a similarly motivated
problem on Massive Open Online Course (MOOC) discussion forums on
recommending salient student discussions for instructors to intervene
given their limited bandwidth.
- Kokil Jaidka - email@example.com
Dr Kokil Jaidka is a postdoctoral researcher in Computer Science
and Chief Technology Officer for the World Wellbeing Project at the
University of Pennsylvania. She has been the lead coordinator of all
aspects of the CL-SciSumm Shared Task since 2014, and she also
co-organized the 1ST BIRNDL workshop. She has expertise working on
large datasets using machine learning and unsupervised approaches on
textual data, and in the specific areas of multi-document summarization
and applied linguistics. She is a reviewer for Scientometrics, Applied
Linguistics and Aslib journal of Information Processing \& Management.
Her PhD dissertation involved the development of a literature review
framework for the summarization of research papers. Currently, she is
conducting social media analyses and user language modeling for opinion
mining, behavioral profiling and health outcomes.
- Min-Yen Kan - firstname.lastname@example.org
My research interests fall under the areas of digital libraries,
natural language processing, information retrieval, human-computer
interaction. Specifically, they include document structure acquisition,
verb analysis, digital library resource annotation and and applied
text summarization. My research goal aims to investigate how natural
language processing and information retrieval can be applied to
improve scholarly publication and knowledge discovery.