@inproceedings{sivagnanam-etal-2025-cutn,
    title = "{CUTN}{\_}{B}io at {B}io{L}ay{S}umm: Multi-Task Prompt Tuning with External Knowledge and Readability adaptation for Layman Summarization",
    author = "Sivagnanam, Bhuvaneswari  and
      C H, Rivo Krishnu  and
      Chauhan, Princi  and
      Rajiakodi, Saranya",
    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.32/",
    doi = "10.18653/v1/2025.bionlp-share.32",
    pages = "269--274",
    ISBN = "979-8-89176-276-3",
    abstract = "In this study, we presented a prompt based layman summarization framework for the biomedical articles and radiology reports developed as part of the BioLaySumm 2025 shared task at the BioNLP Workshop, ACL 2025. For Subtask 1.1 (Plain Lay Summarization), we utilized the abstract as input and employed Meta-LLaMA-3-8B-Instruct with a Tree-of-Thought prompting strategy and obtained 13th rank. In Subtask 1.2 (Lay Summarization with External Knowledge), we adopted an extractive plus prompt approach by combining LEAD-K sentence extraction with Meta-LLaMA-3-8B-Instruct. Medical concepts were identified using MedCAT, and their definitions were taken from Wikipedia to enrich the generated summaries. Our system secured the 2nd position in this subtask. For Subtask 2.1 (Radiology Report Translation), we implemented a Retrieval-Augmented Generation (RAG) approach using the Zephyr model to convert professional radiology reports into layman terms, achieved 3rd place in the shared task."
}