[InetBib] [1st CfP] SDP@EMNLP 2020: 1st Workshop on Scholarly Document Processing and Shared Tasks (SDP 2020)

["Mayr-Schlegel, Philipp via InetBib" <inetbib@xxxxxxxxxx>]

Dear colleagues,


You are invited to participate in the 1st Workshop on Scholarly Document 
Processing (SDP 2020) to be held in conjunction with the 2020 Conference in 
Empirical Methods in Natural Language Processing (EMNLP 2020) on November 11 or 
12 in Punta Cana, Dominican Republic.


The workshop will consist of a research track and a shared task track. The 
shared task track includes the 6th edition of the CL-SciSumm shared task 
(https://github.com/WING-NUS/scisumm-corpus) and two new summarization tasks -- 
CL-LaySumm and LongSumm -- geared towards easier access to scientific methods 
and results.



The tentative submission deadline is July 15, 2020.


SDP is a continuation of the BIRNDL (https://philippmayr.github.io/BIRNDL-WS/) 
and WOSP (https://wosp.core.ac.uk/) workshop series.


Workshop Date and Venue: November 11/12, Punta Cana, Dominican Republic

Website: https://ornlcda.github.io/SDProc/

A tweet to this cfp is also on Twitter: Please share!

<https://twitter.com/SDProc/status/1235405786068602880>



** Introduction **


In addition to the long-standing challenge faced by scholars of keeping up with 
the growing literature in their own and related fields, they must now compete 
with malign pseudo-science and dis-information in informing public policy and 
behavior. This has stimulated workshops and research focused on enhancing 
search, retrieval, summarization, and analysis of scholarly documents. However, 
the general research community on scholarly document processing remains 
fragmented, and efforts towards natural language understanding of scholarly 
text that is central to vastly improve all the said downstream applications are 
not widespread.


To address these gaps, we, the organizers of BIRNDL and WOSP workshops, propose 
the first Workshop on Scholarly Document Processing. We seek to reach to the 
broader NLP and AI/ML community to pool the distributed efforts to improve 
scholarly document understanding and enable intelligent access to the published 
research. The goal of SDP is two-fold: to increase collaboration between 
communities interested in leveraging knowledge stored in scientific literature 
and data and to establish SDP as the single-focused primary venue for the field.


We seek to appeal to the mainstream NLP and ML community working on SDP tasks – 
which are NLP tasks – to publish at SDP as we seek to establish SDP as the 
integrated premier venue. We have established a steering committee to help us 
turn SDP into a conference in the forthcoming years.


** Topics of Interest **


We invite submissions from all communities interested in natural language 
processing, information retrieval, and data mining problems in scientific 
documents; and in processing scientific documents for easier access to various 
audiences. The topics of interest include, but are not limited to:


· Information extraction, text mining and parsing scholarly literature

· Reproducibility and peer review

· Lay Summarization (i.e., summaries created for non-experts) of individual and 
collections of scholarly documents

· Discourse modeling and argument mining

· Summarization and question-answering for scholarly documents

· Semantic and network-based indexing, search and navigation in structured text

· Graph analysis/mining including citation and co-authorship networks

· Analysing and mining of citation contexts for document understanding and 
retrieval

· New scholarly language resources and evaluation

· Connecting and interlinking publications, data, tweets, blogs or their parts

· Disambiguation, metadata extraction, enrichment, and data quality assurance 
for scholarly documents

· Bibliometrics, scientometrics, and altmetrics approaches and applications

· Other aspects of scientific workflows including open access/science, and 
research assessment

· Infrastructures for accessing scientific publications and/or research data


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

(Organisers: Muthu Kumar Chandrasekaran)


CL-SciSumm is the first medium-scale shared task on scientific document 
summarization, with over 500 annotated documents. Last year's CL-SciSumm shared 
task introduced large scale training datasets, both annotated from ScisummNet 
and auto-annotated. For the task, Systems were provided with a Reference Paper 
(RP) and 10 or more Citing Papers (CPs) that all contain citations to the RP, 
which they used to summarise RP. This was evaluated against abstract and human 
written summaries on ROUGE.


The task is defined as follows:

· 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.

This year, CL-SciSumm '20 will have two new tracks: LaySumm and LongSumm.


** CL-LaySumm 2020: The 1st Computational Linguistics Lay Summary Challenge 
Shared Task **

(Organisers: Anita De Waard, Ed Hovy)


To ensure and increase the relevance of science for all of society and not just 
a small group of niche practitioners, researchers have been increasingly tasked 
by funders and publishers to outline the scope of their research for the 
general public by writing a summary for a lay audience, or lay summary. The 
LaySumm summarization task considers automating this responsibility, by 
enabling systems to automatically generate lay summaries. A lay summary 
explains, succinctly and without using technical jargon, what the overall 
scope, goal and potential impact of a scientific paper is.


The corpus for this task will comprise full-text papers with lay summaries, in 
a variety of domains, and from a number of journals. Elsevier will make 
available a collection of Lay Summaries from a multidisciplinary collection of 
journals, as well as the abstracts and full text of these journals.


The task is defined as follows:

· Given: A full-text paper, its Abstract, and a Lay Summary of a given paper

· Task: For each paper, generate a Lay Summary of the specified length


Evaluation


The Lay Summary Task will be scored by using several ROUGE metrics to compare 
the system output and the gold standard Lay Summary. As a follow-up to the 
intrinsic evaluation, we will crowdsource a number of automatically generated 
lay summaries to a panel of judges and a lay audience. Details of the 
crowdsourcing evaluation will be announced with the sharing of the final test 
corpus on July 1st.


All nominated entries will be invited to publish a paper in Open Access 
(Author-Payment Charges will be waived) in a selected Elsevier publication. 
Authors will be asked to provide an automatically generated lay summary of 
their paper, together with their contribution.


** LongSumm 2020: Shared Task on Generating Long Summaries for Scientific 
Documents **

(Organisers: Michal Shmueli-Scheuer, Guy Feigenblat)


Most of the work on scientific document summarization focuses on generating 
relatively short summaries (250 words or less). While such a length constraint 
can be sufficient for summarizing news articles, it is far from sufficient for 
summarizing scientific work. In fact, such a short summary resembles more to an 
abstract than to a summary that aims to cover all the salient information 
conveyed in a given text. Writing such summaries requires expertise and a deep 
understanding in a scientific domain, as can be found in some researchers’ 
blogs.


The LongSumm task opted to leverage blogs created by researchers in the NLP and 
Machine learning communities and use these summaries as reference summaries to 
compare the submissions against.


The corpus for this task includes a training set that consists of 1705 
extractive summaries and around 700 abstractive summaries of NLP and Machine 
Learning scientific papers. These are drawn from papers based on video talks 
from associated conferences (Lev et al. 2019 
TalkSumm<https://arxiv.org/abs/1906.01351>) and from blogs created by NLP and 
ML researchers. In addition, we create a test set of abstractive summaries. 
Each submission is judged against one reference summary (gold summary) on ROUGE 
and should not exceed 600 words.


** Submission Information **


Authors are invited to submit full and short papers with unpublished, original 
work. Submissions will be subject to a double-blind peer review process. 
Accepted papers will be presented by the authors at the workshop either as a 
talk or a poster. All accepted papers will be published in the workshop 
proceedings.


The submissions should be in PDF format and anonymized for review.

All submissions must be written in English and follow the EMNLP 2020 formatting 
requirements<https://2020.emnlp.org/call-for-papers>. EMNLP will make it 
available soon.


Long paper submissions: up to 8 pages of content, plus unlimited references.


Short paper submissions: up to 4 pages of content, plus unlimited references.


Final versions of accepted papers will be allowed 1 additional page of content 
so that reviewer comments can be taken into account.



Submission Website: Submission is electronic, using the Softconf START 
conference management system. EMNLP will make it available soon.


Shared Task registration: Participants of all shared tasks need to register 
here<https://docs.google.com/forms/d/e/1FAIpQLScfHzByrog-k299qBuCp3SbPWcb905_kmOWMvHpDH57VLpVrg/viewform>
 before March 31st, 2020


** Important Dates **


Research track:


Submission deadline – July 15, 2020

Notification of Acceptance – August 17, 2020

Camera-ready submission due – August 31, 2020

Workshop – November 11 or 12, 2020

Shared task track:


Training set release – Feb 15, 2020

Deadline for registration and short systems description – March 31, 2020

Test set release (Blind) – July 1, 2020

System runs due – August 1, 2020

Preliminary system reports due – August 16, 2020

Camera-ready submission due – August 31, 2020

Workshop – November 11 or 12, 2020


The dates are at this stage indicative only and can change.


** Keynote Speakers **


1.     Kuansang 
Wang<https://www.microsoft.com/en-us/research/people/kuansanw/>, Managing 
Director, Microsoft Research Outreach Academic Services

2.     The second keynote speaker will be announced shortly


** Journal Extension **


In the past, the accepted authors were invited to submit an extended version of 
their work to a special issue of a selected journal. The organizers are 
currently in the process of identifying appropriate journals to host a similar 
special issue this year. Relevant updates including topics and requirements for 
this special issue will be shared on the workshop website in due time.


** Organizing Committee **


Muthu Kumar Chandrasekaran, Amazon, Seattle, USA

Anita de Waard, Elsevier, USA

Guy Feigenblat, IBM Research AI, Haifa Research Lab, Israel

Dayne Freitag, SRI International, San Diego, USA

Tirthankar Ghosal, Indian Institute of Technology Patna, India

Drahomira Herrmannova, Oak Ridge National Laboratory, USA

Eduard Hovy, Research Professor, LTI, Carnegie Melon University, USA

Petr Knoth, Open University, UK

David Konopnicki, IBM Research AI, Haifa Research Lab, Israel

Philipp Mayr, GESIS – Leibniz Institute for the Social Sciences, Germany

Robert M. Patton, Oak Ridge National Laboratory, USA

Michal Shmueli-Scheuer, IBM Research AI, Haifa Research Lab, Israel

Dominika Tkaczyk, Crossref, UK


** Steering Committee **


Edward Fox, Professor, Department of Computer Science and Director, Digital 
Library Research Laboratory, Virginia Tech

C. Lee Giles, David Reese Professor, College of Information Sciences and 
Technology, Pennsylvania State University

Min-Yen Kan, Associate Professor, School of Computing, National University of 
Singapore

Dragomir Radev, A. Bartlett Giamatti Professor of Computer Science, Yale 
University

Jie Tang, Professor and Associate Chair of the Department of Computer Science 
and Technology, Tsinghua University

Alex Wade, Group Technical Program Manager, Chan Zuckerberg Initiative

Kuansang Wang, Managing Director, Microsoft Research Outreach Academic Services

Bonnie Webber, Professor, School of Informatics, University of Edinburgh


** Programme Committee **


1.     Akiko Aizawa, National Institute of Informatics, Japan

2.     Colin Batchelor, Cambridge, UK

3.     Joeran Beel, Trinity College Dublin, Ireland

4.     Katarina Boland, GESIS, Germany

5.     Guillaume Cabanac, University of Toulouse, France

6.     Cornelia Caragea, University of illinois at Chicago, US

7.     Zeljko Carevic, GESIS, Germany

8.     Tanmoy Chakraborty, IIIT Delhi, India

9.     Richard Eckart de Castilho, TU Darmstadt, Germany

10.  Helena Deus<https://www.linkedin.com/in/helenadeus>, Elsevier Labs

11.  Daniel Duma, University of Edinburgh, UK

12.  Ed A. Fox, Virginia Tech, USA

13.  Norbert Fuhr, University of Duisburg, Germany

14.  C. Lee Giles, Penn State University, USA

15.  Bela Gipp, University of Wuppertal, Germany

16.  Goran Glavas, University of Mannheim, Germany

17.  Hannaneh Hajishirzi, University of Washington, USA

18.  Monica Ihli, University of Tennessee Knoxville, USA

19.  Ameni Kacem, iCOVER, France

20.  Roman Kern, Graz University of Technology, Austria

21.  Atsushi Keyaki, Denso IT Laboratory Inc, Tokyo, Japan

22.  Martin Klein, Los Alamos National Laboratory, USA

23.  Ilia Kuznetsov, Techn. Univ. Darmstadt, Germany

24.  Birger Larsen, Aalborg University, Denmark

25.  Anne Lauscher, University of Mannheim, Germany

26.  Paolo Manghi, National Research Council of Italy, Italy

27.  Bruno Martins, University of Lisbon, Portugal

28.  Norman Meuschke, University of Wuppertal, Germany

29.  Diego Molla-Aliod, Macquarie University, Australia

30.  Preslav Nakov, Qatar Computing Research Inst., Qatar

31.  Federico Nanni, University of Mannheim, Germany

32.  Jumana Nassour, Ben-Gurion University, Israel

33.  Paco Nathan, Derwen Inc., US

34.  Manabu Okumura, Tokyo Institute of Technology, Japan

35.  Francesco Osborne, Open University, UK

36.  Arzucan Ozgur, Bogazici University, Turkey

37.  Sujit Pal, Elsevier Labs

38.  Rajesh Piryani, South Asian University, India

39.  Silvio Peroni, University of Bologna, Italy

40.  Sujit Pal, Elsevier Labs

41.  Animesh Prasad, Amazon, Cambridge, UK

42.  Horacio Saggion, Universitat Pompeu Fabra, Spain

43.  Angelo Antonio Salatino, The Open University, UK

44.  Philipp Schaer, TH Cologne, Germany

45.  Vivek Kumar Singh, Banaras Hindu University, India

46.  Kazunari Sugiyama, Kyoto University, Japan

47.  Saeed Ul Hassan, IT University, Pakistan

48.  Lucy Vanderwende, University of Washington, USA

49.  Stephen Wan, CSIRO, Australia

50.  Bonnie Webber, University of Edinburgh, UK

51.  Ivana Williams, Chan Zuckerberg Initiative, USA

52.  Dietmar Wolfram, University of Wisconsin-Milwaukee, USA

53.  Jian Wu, Old Dominion University, USA


More details available on the workshop website:

https://ornlcda.github.io/SDProc/


With kind regards,

SDP 2020 organizing committee

Muthu Kumar Chandrasekaran
Research Scientist II
Amazon, Day 1
2121 7th Ave,
Seattle, WA 98121
LinkedIn <https://linkedin.com/in/cmkumar/> | Google Scholar 
Profile<https://scholar.google.com/citations?user=TNXPTz0AAAAJ&hl=en>