@inproceedings{wilson-etal-2025-raincitynlp,
    title = "{R}ain{C}ity{NLP} at {B}io{L}ay{S}umm2025: Extract then Summarize at Home",
    author = "Wilson, Jen  and
      Pollack, Michael  and
      Edwards, Rachel  and
      Bellamy, Avery  and
      Salgi, Helen",
    editor = "Soni, Sarvesh  and
      Demner-Fushman, Dina",
    booktitle = "Proceedings of the 24th Workshop on Biomedical Language Processing (Shared Tasks)",
    month = aug,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.bionlp-share.24/",
    doi = "10.18653/v1/2025.bionlp-share.24",
    pages = "190--195",
    ISBN = "979-8-89176-276-3",
    abstract = "As part of the BioLaySumm shared task at ACL 2025, we developed a summarization tool designed to translate complex biomedical texts into layperson-friendly summaries. Our goal was to enhance accessibility and comprehension for patients and others without specialized medical knowledge. The system employed an extractive-then-abstractive summarization pipeline. For the abstractive component, we experimented with two models: Pegasus-XSum and a Falcons.ai model pre-trained on medical data. Final outputs were evaluated using the official BioLaySumm 2025 metrics. To promote practical accessibility, we completed all experimentation on consumer-grade hardware, demonstrating the feasibility of our approach in low-resource settings."
}