Overview of the Third Workshop on Scholarly Document Processing (2022)

Abstract: With the ever-increasing pace of research and high volume of scholarly communication, scholars face a daunting task. Not only must they keep up with the growing literature in their own and related fields, scholars increasingly also need to rebut pseudo-science and disinformation. These needs have motivated an increasing focus on computational methods for enhancing search, summarization, and analysis of scholarly documents. However, the various strands of research on scholarly document processing remain fragmented. To reach out to the broader NLP and AI/ML community, pool distributed efforts in this area, and enable shared access to published research, we held the 3rd Workshop on Scholarly Document Processing (SDP) at COLING as a hybrid event ( https://sdproc.org/2022/ ). The SDP workshop consisted of a research track, three invited talks and five Shared Tasks: 1) MSLR22: Multi-Document Summarization for Literature Reviews, 2) DAGPap22: Detecting automatically generated scientific papers, 3) SV-Ident 2022: Survey Variable Identification in Social Science Publications, 4) SKGG: Scholarly Knowledge Graph Generation, 5) MuP 2022: Multi Perspective Scientific Document Summarization. The program was geared towards NLP, information retrieval, and data mining for scholarly documents, with an emphasis on identifying and providing solutions to open challenges.

## 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 ) (cabanac et al., 2020; @xcite , the cl-scisumm shared task, and the international workshop on mining scientific publications (wosp foot_1 ) @xcite have established themselves as the principal venues for research in scholarly document processing (sdp). 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. in 2020, the first scinlp workshop foot_2 was held online at the akbc 2020 conference; the workshop brought together interested parties in a talk series focused on various aspects of scientific nlp. the first scholarly document processing (sdp) workshop then took place in co-location with the emnlp 2020 conference as an online workshop (see overview in chandrasekaran et al. (2020)), and provided a dedicated venue for those working on sdp to submit and discuss their research. following these successes and the clear appetite for venues to foster discussions around scholarly nlp, sdp 2021 co-located at naacl, again aimed to connect researchers and practitioners from different communities working with scientific literature and data and created a premier meeting point to facilitate discussions on open problems in sdp. ## keynotes this year, we had 3 keynote speakers discussing a variety of recent advancements in scholarly document processing: min-yen kan (national university of singapore), sophia ananiadou (university of manchester), and andrew head (university of pennsylvania). more talk info provided below: title "scholarly document processing research in the age of ais". ## research track we invited submissions from all communities demonstrating usage of and challenges associated with natural language processing, information retrieval, and data mining of scholarly and scientific documents. relevant topics included: in total, we accepted 18 submissions for the research track for presentation. ## workshop overview and outlook the organizers were gratified by both the size and breadth of the response to the third edition of sdp. the subjects of accepted papers ranged from end uses of the scholarly literature (such as search, document expansion, or writing support) to challenges associated with automated understanding (such as metadata extraction and disambiguation or argument mining), to adaptations of recent successes in the broader field of nlp. it is apparent that automated processing of the scholarly literature is a problem that meets with substantial interest. and it seems likely that we are observing the beginnings of a research community with a narrow enough focus to make rapid progress, but a broad enough set of concerns to offer ample opportunities for cross-pollination. to a first approximation, we regard sdp as a confluence of three communities: nlp, information retrieval, and scientometrics. given our colocation with coling, it is perhaps not surprising that the majority of our submissions emphasized nlp. as we consider future iterations of the workshop, we are discussing ways to increase its subject diversity. with sdp 2022 we have begun to present a more varied set of shared tasks, each highlighting challenges unique to the automated processing of the scholarly literature. as we proceed with planning and advertising, a key objective will be to elicit high-quality submissions from researchers interested in the uses and metalinguistic aspects of scholarly communication. ## conclusion the scholarly literature has long served as a rich source of interesting and challenging problems for computer science, and there is substantial prior work in information retrieval, scientometrics, data mining, and computational linguistics, but many important challenges remain. in many respects, our efforts to faithfully capture the semantics of scholarly communication through automated means are still in their infancy. at the same time, recent events regarding misinterpretation of scholarly information accentuate the importance of better approaches to the automated processing of scholarly literature. by drawing attention to these problems and offering a forum for interested scientists from a range of disciplines to collaborate, we hope that this and future instances of sdp encourage the application of recent advances in relevant fields to this problem area, identify new use cases or improve our understanding of existing ones, and ultimately foster solutions that improve the practice of scholarship and serve society.