Beyond One-Size-Fits-All: Multi-Agent Refinement Framework for Persona-Based Biomedical Summarization

Abstract

Lay summarization aims to make biomedical research accessible to non-experts, but most approaches assume a uniform audience, overlooking variation in medical literacy and information needs. We present MAPS (Multi-Agent Persona-based Summarization), a framework that generates persona-specific summaries through iterative cross-agent feedback. Human evaluation shows MAPS improves quality over single-agent baselines, while automatic metrics fail to capture these gains. LLM-based judges also exhibit limited sensitivity, assigning inflated scores and misdetecting errors. These findings highlight the need for improved evaluation methods for persona-based summarization.

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