Introduction

Due to the fast growth of scientific publications, keeping abreast of new findings and recognizing unsolved challenges are becoming more difficult for researchers in various fields. It is thus necessary to be equipped with state-of-the-art technologies to effectively combine precious findings from diverse scientific documents into one easily accessible resource. Due to its importance, there have been some efforts to achieve this goal for scientific document understanding (SDU). However, despite all of the recent progress, the fragmented research focusing on different aspects of this domain necessitates a forum for researchers from different perspectives to discuss achievements, new challenges, new resource requirements, and impacts of scientific document understanding on various fields. Furthermore, the recent introduction of advanced resources and tools designed for the processing of scientific documents, such as large language models (LLMs) and generative AI systems like Galactica (Taylor et al. 2022) and Med-PaLM (Singhal et al. 2023), opens up new opportunities to advance research and applications of scientific document understanding. The SDU workshop is thus designed to specifically address these gaps for the scientific community. In addition to the recent focus on scholarly text processing and document understanding in natural language processing, this workshop extends SDU to other scientific areas, including but not limited to scientific image processing, automatic programming, knowledge graph manipulation, and data management. We hope that this workshop will foster collaborations with researchers working on different scientific and AI areas for SDU.

Topics of Interest

The goal of the SDU workshop is to gather insights into recent advances and remaining challenges in scientific document understanding (SDU). To this end, the topics of interest for this workshop include but are not limited to:

• Information extraction and information retrieval for scientific documents

• Question answering and question generation for scholarly documents

• Word sense disambiguation, acronym identification, expansion, and definition extraction

• Developing LLMs/generative AI models specific to scientific domains

• Instruction tuning and in-context learning with LLMs for scientific documents

• Document summarization, text mining, document topic classification, and machine reading comprehension for scientific documents

• Graph analysis applications including knowledge graph construction and representation, graph reasoning, and query knowledge graphs

• Multi-modal and multi-lingual scholarly text processing

• Biomedical image processing, scientific image plagiarism detection, and data visualization

• Code/Pseudo-code generation from text and image/diagram captioning

• New language understanding resources such as new syntactic/semantic parsers, language models, or techniques to encode scholarly text

• Survey or analysis papers on scientific document understanding and new tasks and challenges related to each scientific domain

• Factuality, data verification, and anti-science detection

Important Dates

• Paper submission deadline:  November 24, 2023 (11:59 PM)

• Notification date: December 22, 2023

• Camera-ready submissions due: January 5, 2024 (11:59 PM)

• SDU workshop at AAAI 2024: February 26, 2024

All deadlines are “anywhere on earth” (UTC-12)

Submission

Authors are invited to submit their unpublished work that represents novel research. The papers should be written in English using the CEUR Template and follow the CEUR Workshop Proceedings formatting guidelines. Authors can also submit supplementary materials, including technical appendices, source codes, datasets,  and multimedia appendices. All submissions, including the main paper and its supplementary materials, should be fully anonymized. All papers will be double-blind peer-reviewed. 

SDU accepts two types of papers (Note: we don't enforce any hard page limit):

1. Long technical papers with a recommended length of up to 10 pages + references

2. Short papers with a recommended length between 3 and 5 pages + references

Two reviewers with the same technical expertise will review each paper. Authors of the accepted papers will present their work in either the Oral or Poster session.  At least one author of each accepted paper should register at the conference and present the work at the workshop. Submission should be done electronically in PDF format via Microsoft CMT. Sumitted papers can be made available as preprints on Arxiv at any time. SDU will not accept any submission from other mechanisms such as Email. 

References

Ross Taylor, Marcin Kardas, Guillem Cucurull, Thomas Scialom, Anthony Hartshorn, Elvis Saravia, Andrew Poulton, Viktor Kerkez, and Robert Stojnic. 2022. Galactica: A Large Language Model for Science. arXiv preprint arXiv:2211.09085.

Karan Singhal, Shekoofeh Azizi, Tao Tu, S. Sara Mahdavi, Jason Wei, Hyung Won Chung, Nathan Scales, Ajay Tanwani, Heather Cole-Lewis, Stephen Pfohl, Perry Payne, Martin Seneviratne, Paul Gamble, Chris Kelly, Nathaneal Scharli, Aakanksha Chowdhery, Philip Mansfield, Blaise Aguera y Arcas, Dale Webster, Greg S. Corrado, Yossi Matias, Katherine Chou, Juraj Gottweis, Nenad Tomasev, Yun Liu, Alvin Rajkomar, Joelle Barral, Christopher Semturs, Alan Karthikesalingam, and Vivek Natarajan. 2023. Large Language Models Encode Clinical Knowledge. In Nature 620.