Overview of the First Workshop on Scholarly Document Processing ( SDP ) (2020)

Abstract: Next to keeping up with the growing literature in their own and related fields, scholars increasingly also need to rebut pseudo-science and disinformation. To address these challenges, computational work on enhancing search, summarization, and analysis of scholarly documents has flourished. However, the various strands of research on scholarly document processing remain fragmented. To reach to the broader NLP and AI/ML community, pool distributed efforts and enable shared access to published research, we held the 1st Workshop on Scholarly Document Processing at EMNLP 2020 as a virtual event. The SDP workshop consisted of a research track (including a poster session), two invited talks and three Shared Tasks (CL-SciSumm, Lay-Summ and LongSumm), geared towards easier access to scientific methods and results. Website : https://ornlcda.github.io/SDProc

## workshop description over the past several years and at various venues, the joint workshop on bibliometric-enhanced ir and nlp for digital libraries (birndl foot_0 ) @xcite , the cl-scisumm shared task @xcite , and the international workshop on mining scientific publications (wosp foot_1 ) have established themselves as the principal venues for scholarly document processing (sdp) research. however, as these venues are collocated with conferences that are not focused on nlp, current solutions in this domain lag behind modern techniques generated by the greater nlp community. the goal of sdp 2020 was to help foster crossfertilization of ideas by bringing together people from different communities to leverage work on scientific literature and data. in doing so, we hope to create a premier meeting point to facilitate discussions converging towards solutions to open problems in sdp. we believe that acl events are the most appropriate venue for the sdp workshop for two reasons. first, acl events are the premier venues for the confluence of nlp and ml and most of the cornerstone tasks in processing scholarly documents are nlp tasks. improving machine understanding of scholarly semantics embedded in research papers is essential to further many tasks and applications in scholarly document processing. acl events would, therefore, help integrate the broader nlp and ai/ml community with the distributed efforts in scholarly ir and data mining such that this field can progress as a more unified community. from our previous foray with ir and data mining we are convinced that delving into the language model of scholarly artefacts and improving machine understanding of scholarly semantics embedded in research papers is essential to further many tasks and applications in scholarly document processing. second, we seek to bring together researchers and practitioners from various backgrounds focusing on different aspects of scholarly document processing. we believe the interdisciplinary nature of acl venues would greatly assist in encouraging submissions from a diverse set of fields. topics. the topics of interest to sdp encompass all approaches to mining scholarly data and encourage submissions from all relevant communities, including: workshop agenda. the sdp 2020 workshop foot_2 consisted of: two keynote talks, a research track (including a poster session) and a shared task track with 3 separate shared tasks. keynotes. (1) kuansan wang, managing director, microsoft research outreach academic services gave the first keynote titled: "mitigating scholarly corpus biases with citations: a case study on cord-19". abstract: with the broad adoption of evidence based decision making processes, recent years have witnessed more frequent examples where biases in the datasets or the analytical algorithms lead to unfortunate and sometimes harmful outcomes. being mindful of potential biases and actively taking measures to mitigate them have become a necessary second nature for scholars and decision makers alike. citations in scholarly publications have long been known to represent the crowd-sourced collective judgments on scholarly communications and can be a valuable source of information in analyzing scholarly documents. this study describes a methodology that uses citations to identify biases in such corpus, using as an example the covid-19 open research dataset, or cord-19, a corpus created to advance the development of intelligent technologies that can assist scientists in navigating through the voluminous literature of covid-19. by expanding to articles in the citation networks seeded by cord-19 with three distinct algorithms, it can be shown that cord-19 has a strong tilt in favor of recent articles and uneven coverages in the topical fields and the publication venues. using cord-19 to identify critical knowledge and assess the journal importance, for example, will lead to different conclusions from the analyses based on the three expanded datasets, of which results largely agree with one another. cord-19, however, does not appear to exhibit biases in describing research collaborations in terms of team sizes or geolocations. currently, the three citation network traversal algorithms only utilize bibliographic records. how improvements can be made to them, such as through more sophisticated uses of citation contexts, will also be discussed. ## research track in total, we received 34 papers for the research track. we accepted 9 papers for oral presentation (7 as full papers and 2 as short papers). we rejected 14 research paper submissions. in order to include a broader variety of contributions, we decided to invite all research papers with borderline scores as poster papers, leading us to accept 11 posters, which were presented in a separate virtual poster slot. this year, the emnlp organizers added a new resource, the "findings of emnlp" 5 . 520 emnlp papers were accepted to findings of emnlp. some of the authors of these findings papers choose the sdp workshop as primary presentation venue. we accommodated and invited 3 "findings" papers for oral presentation and 1 as a poster. one demo paper was accepted as technical contribution in addition to the scientific program. in the following, we list all contributions which were presented in some form at the workshop. ## organising and steering committees a formal organizing committee and a steering committee helped guide the successful organisation first sdp. we thank all members for their help in reviewing all submissions, submitting the workshop proposal, and planning the final program: c. lee giles, pennsylvania state university, usa; min-yen kan, national university of singapore; petr knoth, open university, uk; robert patton, oak ridge national laboratory, usa; dragomir radev, yale university, usa; jie tang, tsinghua university, china; kuansan wang, msr outreach academic services, usa; bonnie webber, university of edinburgh, uk. ## conclusion the scholarly literature has long served as a rich source of interesting and challenging problems for computer science. recent events regarding misinterpretation of scholarly information accentuate the importance of better approaches to the automated processing of scholarly literature. we hope that this event helps to connect these challenges to use cases, fostering solutions that ultimately improve the practice of scholarship and serve society.