The 5th Computational Linguistics Scientific Document Summarization Shared Task (CL-SciSumm 2019)

Results will be presented at the BIRNDL 2019 Workshop,
Task Sponsored by SRI International and Chan Zuckerberg Initiative

Last updated: Wed May 1 06:45:39 SGT 2019
  • Prof. Bonnie Webber, U.Edinburgh will deliver the SRI Distinguished Keynote!
  • Alex Wade from Chan-Zuckerberg Initiative (CZI) will deliver a keynote! at BIRNDL!
  • We are still accepting registrations although registered teams have already starting building their system since March 30! Just go ahead and register and get started!
  • CL-SciSumm 2019 has been accepted to be colocated with ACM SIGIR 2019, Paris! Thanks to the PC and participants for their continued support!
  • CL-SciSumm 2019 has been proposed to be colocated with ACM SIGIR 2019, Paris!.


Call for Participation

You are invited to participate in the CL-SciSumm Shared Task at BIRNDL 2019. The shared task will be on automatic paper summarization in the Computational Linguistics (CL) domain.

This task follows up on the successful CLScisumm-18 co-located with SIGIR 2018 and three editions prior to that. Over the four editions, a training corpus of forty topics from CL research papers have been released. Participants were invited to enter their systems in a task-based evaluation. We also released the manually 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 2019 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 simplification. As before, we will have more training data and a blind test set for evaluation.

SRI International 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 reference paper.

Organizations wishing to participate in the Shared Task track at BIRNDL 2019 are invited to register on EasyChair by March 30.

Register Here

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.


Corpus Training set for this year's task is available on github here.
You can also download the training set here.
Test set is available on github: Test-Set-2018 on github

The corpus for the CL-SciSumm task has been created by randomly sampling 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 specified below.

The manually annotated training set of 40 articles and citing papers is already available for download and can be used by participants to pilot their systems. Further, this year we have introduced 1000 documents sets that were automatically annotated to be used as training data. This training data was generated following Nomoto,2018. Further, for Task 2 one thousand summaries that were released as part of the SciSummNet (Yasunaga et al., 2019) have been included as human summaries to train on. The test set of 20 articles is available in 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.



Important Dates

Please consult the BIRNDL Workshop for official dates for the workshop.

Training Set ReleaseAlready Online
Deadline for Registration and Short System DescriptionsMarch 30, 2019 (we are open for further registrations although you should know that teams have started building their systems).
Test Set ReleaseAlready Online
System Runs DueMay 24, 2019
Preliminary System Reports Due in EasyChairJune 9, 2019
Camera-Ready Contributions Due in EasyChairJuly 7, 2019
Participant Presentations at BIRNDL 2019, Paris July 25, 2019

All deadlines 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 CL-SciSumm Shared Task Series 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 2019 Workshop

The BIRNDL 2019 workshop will be held on . 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. Shared Task 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 session.



Organising Committee

Muthu Kumar Chandrasekaran -

is an Advanced Computer Scientist, Machine Learning at SRI International's Artificial Intelligence Center. Previously he was a Ph.D. student at NUS School of Computing. He is broadly interested in natural language processing, machine learning and their applications to information retrieval; specifically, in retrieving and organising information from asynchronous conversation media such as scholarly publications and discussion forums. He has been co-organizing the CL-SciSumm Shared Task series and the BIRNDL workshop series since 2014. He also reviews for ACL, EMNLP, NAACL and JCDL conferences. During his PhD he also spent time at the Allen Institute for Artificial Intelligence's Semantic Scholar research and National Institute of Informatics, Tokyo.

Dayne Freitag -

is the director of the Advanced Analytics group in SRI's Artificial Intelligence Center. His research seeks to apply artificial intelligence to information assimilation, management and exploitation. Specific areas of interest include natural language processing and computational linguistics; machine learning; data mining; information extraction; information retrieval; information diffusion; and information integration. Freitag has served as principal investigator for a number of research projects including several large, multi-institutional efforts. His research goals have focused on the automation of data science; the automatic extension of mechanistic models through machine reading; knowledge federation over diverse information sources through data analytics and natural language processing; explaining the spread of ideas through online communities; and novel approaches to institutional knowledge management using controlled English. Freitag holds a B.A. in English literature from Reed College, and a Ph.D. in computer science from Carnegie Mellon University.

Michihiro Yasunaga -

He is a final year undergraduate student in computer science at Yale University, conducting research in natural language processing (NLP), advised by Prof. Dragomir Radev. His research includes natural language understanding tasks such as summarization and semantic parsing, and the robustness of machine learning techniques in NLP.

Dragomir Radev -

He is a A. Bartlett Giamatti Professor of Computer Science at Yale University. His interests include interests include Natural Language Processing (NLP), Artificial Intelligence, Computational Linguistics, Machine Learning, Information Retrieval, Text Summarization, Network Analysis, Text Mining Applications of NLP to Bioinformatics, Social Network Analysis, Political Science, and the Humanities. He has received numerous awards including Fellow of the ACM (Association for Computing Machinery) (2015), University of Michigan Faculty Recognition Award (2013), Linguistics Society of America: Linguistics, Language and the Public Award (2011) (as co-founder and program chair of NACLO), Secretary of ACL (Association for Computational Linguistics) (2006-2015), The Gosnell Prize for Excellence in Political Methodology (shared) (2006), University of Michigan UROP Faculty Award for Outstanding Research Mentorship (2004).

Min-Yen Kan -

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.