@inproceedings{datta-etal-2026-beyond,
    title = "Beyond Abstracts: A Biomedical {M}e{SH} Indexing Corpus Incorporating Summarized Methods Sections",
    author = "Datta, Sujoy  and
      Mercer, Robert E.  and
      Wang, Xindi",
    editor = "Rehm, Georg  and
      Dietze, Stefan  and
      Dessi, Danilo  and
      Maynard, Diana  and
      Schimmler, Sonja",
    booktitle = "Proceedings of Natural Scientific Language Processing ({NSLP}) @ {LREC} 2026",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/test-year-match/2026.nslp-1.4/",
    doi = "10.63317/4iw4aavrxi9o",
    pages = "32--43",
    abstract = "Automated Medical Subject Heading (MeSH) indexing systems rely predominantly on titles and abstracts, while human indexers at the National Library of Medicine examine full-text articles{---}particularly Methods sections{---}that often contain crucial experimental terminology absent from abstracts. This information asymmetry limits model performance and prevents detection of methodologically-grounded MeSH descriptors. We introduce a novel biomedical MeSH indexing corpus comprising over one million English biomedical articles, each annotated with title, abstract, journal metadata, publication year, expert-curated MeSH terms, and{---}uniquely{---}extractive summaries of Methods sections. Using LLaMA 3 with an iterative re-prompting strategy, we generated high-fidelity summaries. To avoid label leakage, evaluation labels are inferred using journal-specific MeSH frequency profiles rather than gold annotations. This publicly accessible dataset addresses a critical gap in full-text MeSH indexing research. Building upon this resource, we propose an extended multi-channel neural architecture that incorporates Methods-derived representations. Empirical results demonstrate consistent performance gains across both example-based and label-based evaluations, indicating better retrieval of infrequent terms. These findings highlight that procedural knowledge in the Methods section encodes critical semantic cues overlooked by title-abstract only models."
}