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Datasets for Scientific Literature Understanding: A Survey

Paper · 2026 · Yuanzhe Zhang, Xun Zhao, Maodi Hu et al.

Summary

Scholarly publishing is growing faster than researchers can follow, so machine comprehension of papers is a key enabler of AI for Science (AI4S). This survey, by authors at the National Science Library of the Chinese Academy of Sciences, catalogues the datasets behind that goal and arranges them into four branches:

  • Structural understanding: layout, tables, figures and formulas.
  • Text understanding: information extraction, summarization, question answering and argument mining.
  • Multimodal understanding: tables, figures, formulas and joint reasoning.
  • Pre-training/instruction fine-tuning corpora.

The authors also plot a timeline of dataset development. It shows a shift from basic visual parsing of pages toward evidence-focused reasoning, synthesis across documents, and training data aimed at LLMs. They argue that LLMs have raised expectations about data scale, quality and multimodality. The paper ends with open problems and proposed research directions. For practitioners, it works as a map for choosing benchmarks and as a template for designing new datasets. The authors note that coverage of non-English and low-resource settings remains thin, which matters for teams building such resources.

Key points

  • The survey covers literature up to April 2026 and claims to be the first comprehensive survey of this area. It appeared in Findings of ACL 2026, pp. 28369–28389.
  • It names representative resources for each branch:
    • Layout: PubLayNet, DocBank, DocLayNet, OmniDocBench.
    • Tables: SciTSR, PubTabNet, PubTables-1M.
    • QA: QASPER, PubMedQA, LitQA.
    • Multimodal reasoning: MMMU, MMSci, SPIQA, CharXiv.
    • Pre-training: S2ORC, The Pile, Dolma, RedPajama.
    • Instruction tuning: SciInstruct, SciRIFF, SMolInstruct.
  • SciBank provides over 74K human-validated scientific pages annotated with 12 types of layout region. Table datasets still suffer from alignment noise introduced by heuristic pipelines and handle borderless or nested tables poorly.
  • The authors propose four future directions:
    • Scalable annotation that combines LLMs with expert checking.
    • Fine-grained multimodal linking, e.g., tying textual claims to specific chart parts or table cells.
    • Multi-hop reasoning across documents.
    • Standardized, interoperable datasets with traceable provenance.
  • Stated limitations: the survey favours English and high-resource disciplines. It also leaves out AI4S data that does not come from literature, such as simulations, lab notebooks and molecular databases.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🏛️Chinese Academy of SciencesBeijing · China246 works · 24,952 citations🔬National Science LibraryBeijing · China4 works · 16 citations

 Fields, topics and concepts

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksComputer Vision and Pattern Recognition · 3,486 worksBiochemistry, Genetics and Molecular Biology · 263 worksMolecular Biology · 260 works

🧭 Topics 2Research subjects and languages the work is about.Reasoning · 1,685 worksVision & language · 811 works

Cite

Cite as Yuanzhe Zhang, Xun Zhao, Maodi Hu, Xi Sun, Donghuan Song and Zhixiong Zhang. 2026. Datasets for Scientific Literature Understanding: A Survey. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.1414

@inproceedings{zhang2026datasets,
  title = {{Datasets for Scientific Literature Understanding: A Survey}},
  author = {Yuanzhe Zhang, Xun Zhao, Maodi Hu, Xi Sun, Donghuan Song and Zhixiong Zhang},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1414},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1414}
}

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