@inproceedings{gonzalez-sanchez-martinez-2024-hulat,
    title = "{HULAT}-{UC}3{M} at {B}iolay{S}umm: Adaptation of {B}io{BART} and Longformer models to summarizing biomedical documents",
    author = "Gonzalez Sanchez, Adrian  and
      Mart{\'i}nez, Paloma",
    editor = "Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Miwa, Makoto  and
      Roberts, Kirk  and
      Tsujii, Junichi",
    booktitle = "Proceedings of the 23rd Workshop on Biomedical Natural Language Processing",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.bionlp-1.71/",
    doi = "10.18653/v1/2024.bionlp-1.71",
    pages = "780--785",
    abstract = "This article presents our submission to the Bio- LaySumm 2024 shared task: Lay Summarization of Biomedical Research Articles. The objective of this task is to generate summaries that are simplified in a concise and less technical way, in order to facilitate comprehension by non-experts users. A pre-trained BioBART model was employed to fine-tune the articles from the two journals, thereby generating two models, one for each journal. The submission achieved the 12th best ranking in the task, attaining a meritorious first place in the Relevance ROUGE-1 metric."
}