@inproceedings{salvi-etal-2026-beyond,
    title = "Beyond One-Size-Fits-All: Multi-Agent Refinement Framework for Persona-Based Biomedical Summarization",
    author = "Salvi, Rohan Charudatt  and
      Chawla, Chirag  and
      Akhtar, Md. Shad  and
      Yadav, Shweta",
    editor = "Gupta, Deepak  and
      Thompson, Paul  and
      Ananiadou, Sophia  and
      Demner-Fushman, Dina",
    booktitle = "Proceedings of the Third Workshop on Patient-Oriented Language Processing ({CL}4{H}ealth) @ {LREC} 2026",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/test-year-match/2026.cl4health-1.28/",
    doi = "10.63317/22uei8f8k9za",
    pages = "320--332",
    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."
}