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Overview paper for the CL-SciSumm 2020 edition; overview paper also describes the first editions of two new summarization shared tasks introduced in 2020.

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This work is licensed under a Creative Commons Attribution 4.0 International License except for the following files which are closed source under strict copyright laws enforced by Elsevier labs. We hold no accountability for these:

CC BY 4.0

(scisumm-corpus @

This package contains a release of training and test topics to aid in the development of computational linguistics summarization systems.

CL-SciSumm @ EMNLP 2020

The CL-SciSumm Shared Task is run off the CL-SciSumm corpus and is composed of
three sub-tasks in automatic research paper summarization on a new corpus of research papers. A training corpus with summaries for one thousand forty topics and forty topics for citance to reference span id (or provenance identification) tasks has been released. A test corpus of twenty topics is held-out as a blind test-set. The topics comprise of ACL Computational Linguistics and Natural Language Processing research papers, and their citing papers and three output summaries each. The three output summaries comprise: the traditional authors' summary of the paper (the abstract), the community summary (the collection of citation sentences ‘citances’) and a human summary written by a trained annotator. Within the corpus, each citance is also mapped to its referenced text in the reference paper and tagged with the information facet it represents.

The manually annotated training set of 40 articles (Tasks 1a and b) and citing papers, human written summaries (1040 documents) for them and a further 1000 document corpus (ScisummNet), an auto-annotated noisy dataset with several thousands of article-citing paper papers (to aid in ' training deep learning models) are readily available for download and can be used by participants. This data can be found in /data/Training-Set-2019/Task1/From-Training-Set-2018 and /data/Training-Set-2019/Task2/From-Training-Set-2018

README for 1st Long Summarization Task (LongSumm 2020)

Task Description and dataset can be found at: LongSumm Github Repo

README for 1st Lay Summarization Task (LaySumm 2020)

Task Description and a sample dataset can be found at: here in this Github repo.

README for The 6th Computational Linguistics Scientific Document Summarization Shared Task Corpus (CL-SciSumm 2020)

February 15, 2020

NEW Changes for CL-SciSumm 2020

We have no additional training data annotated or auto-annotated for 2020. We will have only the blind test set. However, you may use addional training data from out-of-domain, that is non-computational linguistcs papers, which are available as other public datasets: BIGPATENT (Sharma and Wang, 2019), arXiv dataset (Cohan et al, 2018), pubmed dataset (Cohan et al, 2018)

Updates for CL-SciSumm 2019

In 2019 we had introduced 1000 document sets that were automatically annotated to be used as training data. This training data was generated following Nomoto,2018. This data can be found in /data/Training-Set-2019/Task1/From-ScisummNet-2019. Note that the auto-annotated data is available only for Task 1a. No discourse facet is provided for the classification task: Task1b. We recommend you to use the auto-anootated data only for training 'reference span selection' models for Task 1a and use the manually annotated training data from 40 document sets for Task1b.

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. This data can be found in /data/Training-Set-2019/Task2/From-ScisummNet-2019

The test set of 20 articles is available in /data/Test-Set-2018. This is a blind test set, that is, the ground truth is withheld. 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.

For more details, see the Contents Section at the bottom of this Readme. To know how this corpus was constructed, please see ./docs/corpusconstruction.txt

Results of the CL-SciSumm-20 will be released in the SDP workshop collocated with EMNLP 2020, Go to task website. The workshop will be virtual / online. This is a great opporutnity for teams to participate since you don't need a travel budget to come and present your work!

Last editions proceedings - CL-SciSumm '19 @ SIGIR '19 can be found in BIRNDL Proceedings: under the header 'System Papers'

If you use the data and publish please let us know and cite our CL-SciSumm 2019 task overview paper:

title={Overview and Results: CL-SciSumm Shared Task 2019},<br>
author={Chandrasekaran, Muthu Kumar and Yasunaga, Michihiro and Radev, Dragomir and Freitag, Dayne and Kan, Min-Yen},<br>
booktitle={In Proceedings of Joint Workshop on Bibliometric-enhanced Information Retrieval and NLP for Digital Libraries (BIRNDL 2019)},<br>

Please read further for details on the Computational Linguistics Shared Task run as part of Scholarly Document Processing 2020 workshop collocated with EMNLP 2020 @Virtual/Online, Nov 19 - official website hosted at: SDP website


You are invited to participate in the CL-SciSumm Shared Task at SDP@EMNLP 2020. The shared task will be on automatic paper summarization in the Computational Linguistics (CL) domain. The output summaries will be of two types: faceted summaries of the traditional self-summary (the abstract) and the community summary (the collection of citation sentences ‘citances’). We also propose to group the citances by the facets of the text that they refer to.

This task follows up on the successful previous editions at SIGIR 2019, 2018, 2017, JCDL 2016 and the Pilot Task conducted as a part of the BiomedSumm Track at the Text Analysis Conference 2014 (TAC 2014).

The task is defined as follows:

Given: A topic consisting of a Reference Paper (RP) and upto 10 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 gold standard. Task 2 will be scored using the ROUGE family of metrics between i) the system output and the gold standard summary fromt the reference spans ii) the system output and the asbtract of the reference paper. Again, Task 2 is optional.


This is the open repository for the Scientific Document Summarization Corpus and Annotations contributed to the public by the Web IR / NLP Group at @ the National University of Singapore (WING-NUS) with generous support from Microsoft Research Asia.


This file.


Frequently asked questions on the 2018 shared task including updates to the corpus, annotation format from the previous edition.


README files for the previous editionS of the shared task hosted at BIRNDL@SIGIR 2018, BIRNDL@SIGIR 2017, BIRNDL@JCDL 2016 and TAC2014.


A readme detailing the rules and steps followed to create the document corpus by randomly sampling documents from the ACL Anthology corpus and selecting their citing papers.


Describes the naming convention followed to identify annotation files for each training topic in ./data/???-????_TRAIN/Annotation/


Rules followed to resolve difficult cases in annotation. It can serve as a synopsis of the larger annotation guidelines. For the detailed annotation guidelines, please refer to the details hosted at


References for each of the papers for each of the topics, one file per topic.


Directories containing the Documents, Summaries, and Annotations for each topic, one directory per Topic ID.


This directory contains the 10 source documents for the topic (1 RP and upto 10 CPs), one file per paper, in the original pdf format.


This directory contains the source document for the RP of the topic in XML format in UTF-8 character encoding. The file corresponds to the similarly named pdf file in Documents_PDF/. All annotations and offsets for the topic are with respect to the xml files in this directory. All the files were created from the pdf file using Adobe Acrobat.
Note that there were OCR errors in reading several of the files, and the annotators often had to manually edit the converted txt files. Research groups using are free to use alternative parsing tools on the pdfs provided, if they are found to perform better.


This directory contains the source document for the CPs of the topic in xml format in UTF-8 character encoding. Each file corresponds to the similarly named pdf file above.


This directory contains the annotation files for the topic, from 3 different annotators.
Please DO NOT use older annotations; only use .annv3.txt for the 2016 Shared Task.


The summary task (Task 2) is an optional, "bonus" task which participants may want to attempt. This directory contains the two kinds of summaries - i. the abstract, and ii.human written summaries of the reference paper.


Given a reference paper (RP) and 10 or more citing papers (CPs), annotators from the University of Hyderbad were instructed to find citations to the RP in the CPs. Annotators followed instructions in SciSumm-annotation-guidelines.pdf to mark the Citation Text, Citation Marker, Reference Text, and Discourse Facet for each citation of the RP found in the CP.

Organisers' Contacts

For further information about this data release, contact the following members of the SDP 2020 workshop organising committee:



This README was updated from README2019 by Muthu Kumar Chandrasekaran in Aug, 2020. For revision information, check source code control logs.


Scientific Document Summarization Corpus and Annotations from the WING NUS group.




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