Divide-Prompt-Refine: a Training-Free, Structure-Aware Framework for Biomedical Abstract Generation
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Computer Science > Computation and Language
Title:Divide-Prompt-Refine: a Training-Free, Structure-Aware Framework for Biomedical Abstract Generation
Abstract:Biomedical abstracts play a critical role in downstream NLP applications, such as information retrieval, biocuration, and biomedical knowledge discovery. However, a non-trivial number of biomedical articles do not have abstracts, diminishing the utility of these articles for downstream tasks. We propose DPR-BAG (Divide, Prompt, and Refine for Biomedical Abstract Generation), a training-free, zero-shot framework that generates coherent and factually grounded abstracts for biomedical articles with full text but no abstract. DPR-BAG decomposes full-text documents into structured rhetorical facets following the Background-Objective-Methods-Results-Conclusions (BOMRC) schema, performs parallel LLM-based summarization for each facet, and applies a final refinement stage to restore global discourse coherence. On PMC-MAD, a distribution-aligned dataset of 46,309 biomedical articles, DPR-BAG improves abstractive novelty over strong extractive and fine-tuned baselines, while maintaining factual consistency. Our ablation study reveals a counterintuitive finding: increasing prompt complexity or explicitly injecting entity-level guidance can degrade factual alignment, highlighting the importance of controlled prompting strategies. These findings underscore the potential of training-free, structure-aware frameworks for scalable biomedical abstract generation in low-resource settings. Our data and code are available at this https URL and this https URL.
| Comments: | Accepted by BioNLP 2026 |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2605.20628 [cs.CL] |
| (or arXiv:2605.20628v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.20628
arXiv-issued DOI via DataCite (pending registration)
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