@inproceedings{zhang-etal-2025-aehrc,
    title = "{AEHRC} at {B}io{L}ay{S}umm 2025: Leveraging T5 for Lay Summarisation of Radiology Reports",
    author = "Zhang, Wenjun  and
      Chandra, Shekhar  and
      Koopman, Bevan  and
      Dowling, Jason  and
      Nicolson, Aaron",
    editor = "Soni, Sarvesh  and
      Demner-Fushman, Dina",
    booktitle = "BioNLP 2025 Shared Tasks",
    month = aug,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.bionlp-share.21/",
    pages = "171--178",
    ISBN = "979-8-89176-276-3",
    abstract = "Biomedical texts, such as research articles and clinical reports, are often written in highly technical language, making them difficult for patients and the general public to understand. The BioLaySumm 2025 Shared Task addresses this challenge by promoting the development of models that generate lay summarisation of biomedical content. This paper focuses on Subtask 2.1: Radiology Report Generation with Layman{'}s Terms. In this work, we evaluate two large language model (LLM) architectures, T5-large (700M parameter encoder{--}decoder model) and LLaMA-3.2-3B (3B parameter decoder-only model). Both models are trained under fully-supervised conditions using the task{'}s multi-source dataset. Our results show that T5-large consistently outperforms LLaMA-3.2-3B across nine out of ten metrics, including relevance, readability, and clinical accuracy, despite having only a quarter of the parameters. Our T5-based model achieved the top rank in both the open-source and close-source tracks of the subtask 2.1."
}