Divide-Prompt-Refine: a Training-Free, Structure-Aware Framework for Biomedical Abstract Generation
arXiv:2605.20628v1 Announce Type: new 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-
The proliferation of advanced large language models (LLMs) and the increasing demand for efficient information retrieval in specialized fields like biomedicine are driving the development of training-free, zero-shot generative AI solutions.
This development allows for the automated generation of high-quality abstracts for a vast body of unabstracted biomedical literature, significantly improving knowledge discovery, information accessibility, and the utility of existing research for downstream AI applications.
Previously underutilized biomedical full-text articles can now be effectively summarized and integrated into NLP workflows, changing how researchers and AI systems interact with scientific information.
- · Biomedical researchers
- · NLP developers in life sciences
- · Biocuration platforms
- · AI agents
- · Manual abstracting services
- · Inefficient knowledge discovery methods
Access to a larger, more structured dataset of biomedical information for AI training and analysis.
Accelerated pace of scientific discovery and patenting due to improved information synthesis and retrieval.
Potential for new therapeutic breakthroughs as AI agents can more effectively parse and connect disparate research findings.
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Read at arXiv cs.CL