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

TL;DR

DPR-BAG is a training-free, structure-aware framework that generates biomedical abstracts from full texts by decomposing documents into rhetorical facets, improving factual consistency and novelty without training.

Contribution

It introduces a novel zero-shot, structure-aware approach for biomedical abstract generation that does not require training and leverages document decomposition and parallel summarization.

Findings

Improves abstractive novelty over extractive and fine-tuned baselines.

Maintains factual consistency in generated abstracts.

Explicit entity guidance can degrade factual alignment.

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.