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Beyond Abstracts: A Biomedical MeSH Indexing Corpus Incorporating Summarized Methods Sections

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.