@inproceedings{zhang-etal-2026-datasets,
    title = "Datasets for Scientific Literature Understanding: A Survey",
    author = "Zhang, Yuanzhe  and
      Zhao, Xun  and
      Hu, Maodi  and
      Sun, Xi  and
      Song, Donghuan  and
      Zhang, Zhixiong",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1414/",
    pages = "28369--28389",
    ISBN = "979-8-89176-395-1",
    abstract = "Empowering machines to understand scientific literature is crucial for accelerating scientific discovery and advancing the AI for Science (AI4S) paradigm. In this paper, we present a comprehensive survey of datasets serving this domain. We propose a systematic taxonomy that organizes resources spanning structural understanding, text understanding, multimodal understanding and pre-training/instruction fine-tuning. Beyond a structured overview, we discuss the evolution of the field, elucidating how the emergence of Large Language Models (LLMs) has reshaped research priorities of dataset construction. By synthesizing existing datasets and identifying critical future directions, this work provides a roadmap for advancing intelligent scientific research systems."
}