Overview of the Fourth Workshop on Scholarly Document Processing (2024)

Abstract: The workshop on Scholarly Document Processing (SDP) started in 2020 to accelerate research, inform policy and educate the public on natural language processing for scientific text. The fourth iteration of the workshop, SDP24 was held at the 62nd Annual Meeting of the Association for Computational Linguistics (ACL24) as a hybrid event. The SDP workshop saw a great increase in interest, with 57 submissions, of which 28 were accepted. The program consisted of a research track, four invited talks and two shared tasks: 1) DAGPap24: Detecting automatically generated scientific papers and 2) Context24: Multimodal Evidence and Grounding Context Identification for Scientific Claims. The program was geared towards NLP, information extraction, information retrieval, and data mining for scholarly documents, with an emphasis on identifying and providing solutions to open challenges.

## program the sdp 2024 workshop consisted of four keynote talks, a research track and a shared task track. sdp 2024 received 57 submissions, of which 28 were accepted (49% acceptance rate). since the workshop will be hybrid, there will be both in-person and virtual presentations at the conference venue and online. topics of the presentations run the gamut, and include representation learning of scientific documents (including tables and images), citation text as well as paper text generation, peer review processing, scientific document summarization, ai-generated text detection, scientific intuition and reasoning, scientific papers to proposals, entity recognition and author disambiguation, scientific claim verification, and many more. as expected, we see a sharp increase in papers that employ large language models for downstream sdp tasks. the full program with links to papers, videos and posters is available at https://sdproc.org/2024/program.html . ## keynotes this year we had four keynote lectures from researchers who are prominent in natural language processing for scholarly documents: ## workshop overview and outlook the organizers were gratified by both the size and breadth of the response to the fourth edition of sdp. the subjects of accepted papers ranged from end uses of the scholarly literature to challenges associated with automated understanding to adaptations of recent successes of llms 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 acl, 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 2024 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 meta-linguistic 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.