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Scientific Article Section Classification (SASC) Dataset

Summary

Finding sections in scientific papers by their semantic role (methods, results, and so on) is useful for section-targeted search, focused NLP processing, and building knowledge graphs. However, existing resources mostly address page layout, cover only abstracts, or ignore how sections nest. The authors address this gap with SASC, an openly licensed collection of 4,896 full-text articles obtained through OpenAIRE. Roughly a thousand papers come from each of four fields (Energy, Cancer, Neuroscience, Transport), and a fifth, general-domain split was drawn from a discipline-stratified sample. Licenses were checked against publisher metadata, landing pages, and Unpaywall. The PDFs were converted with GROBID, and a custom TEI parser recorded each section's title, number, text, and depth. Labels were first assigned semi-automatically from section titles and then manually corrected. The labels follow a new two-tier taxonomy. The paper's main contribution is the resource itself rather than model scores: it offers a domain-diverse, hierarchy-aware basis for training and evaluating section classifiers. Benchmark experiments are deferred to later work.

Key points

  • The corpus contains 4,896 papers in 5 splits: four domain splits of about 1,000 papers each (Energy, Cancer, Neuroscience, Transport) and one general-science split.
  • The taxonomy has two levels, with 9 coarse-grained and 47 fine-grained section classes.
  • The processing pipeline uses GROBID for PDF conversion and a custom TEI XML parser. For each body section, the parser extracts the title, the explicit number (e.g., "3.2"), the full text, and the hierarchical level.
  • All sources come from OpenAIRE under permissive licenses (CC-BY or Public Domain), verified via publisher metadata, landing pages, and Unpaywall.
  • The data is released at github.com/sirisacademic/sasc. The paper reports no model benchmarks.

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

Fields: Linguistics & languages

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/3rdo9r4az3iy

@inproceedings{duransilva2026scientific,
  title = {{Scientific Article Section Classification (SASC) Dataset}},
  author = {Duran-Silva, Nicolau and Moreno-Schneider, Julian and Parra-Rojas, César and Rehm, Georg},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/3rdo9r4az3iy},
  url = {https://doi.org/10.63317/3rdo9r4az3iy}
}

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