@inproceedings{basu-etal-2025-suwmit,
    title = "{SUWMIT} at {B}io{L}ay{S}umm2025: Instruction-based Summarization with Contrastive Decoding",
    author = "Basu, Priyam  and
      Cols, Jose  and
      Jarvis, Daniel  and
      Park, Yongsin  and
      Rodabaugh, Daniel",
    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.29/",
    pages = "240--248",
    ISBN = "979-8-89176-276-3",
    abstract = "In the following paper, we present our team{'}s approach to subtask 1.1 of the BioLaySumm 2025 shared task, which entails the automated generation of lay summaries from biomedical articles. To this end, we experiment with a variety of methods for text preprocessing, extractive summarization, model fine-tuning, and abstractive summarization. Our final results are generated on a fine-tuned Llama 3.1 Instruct (8B) model, notably achieving top scores on two out of four relevance metrics, as well as the highest overall ranking among this year{'}s participating teams on the plain lay summarization subtask."
}