Divide-Prompt-Refine: a Training-Free, Structure-Aware Framework for Biomedical Abstract Generation

• URL: http://arxiv.org/abs/2605.20628v1
• Date: Wed, 20 May 2026 02:25:21 GMT
• Title: Divide-Prompt-Refine: a Training-Free, Structure-Aware Framework for Biomedical Abstract Generation
• Abstract summary: We propose DPR-BAG (Divide, Prompt, and Refine for Biomedical Abstract Generation) to generate coherent and factually grounded abstracts for biomedical articles with full text but no abstract.<n>DPR-BAG decomposes full-text documents into structured rhetorical facets following the Background-Objective-Methods-Results-Conclusions (BOMRC) schema.<n>It improves abstractive novelty over strong extractive and fine-tuned baselines, while maintaining factual consistency.
• Score: 0.8774270519266251
• License: http://creativecommons.org/licenses/by/4.0/
• 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 https://huggingface.co/datasets/pmc-mad/PMC-MAD and https://github.com/ScienceNLP-Lab/MultiTagger-v2/tree/main/DPR-BAG.

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