@inproceedings{chen-etal-2023-ncuee,
    title = "{NCUEE}-{NLP} at {B}io{L}ay{S}umm Task 2: Readability-Controlled Summarization of Biomedical Articles Using the {PRIMERA} Models",
    author = "Chen, Chao-Yi  and
      Yang, Jen-Hao  and
      Lee, Lung-Hao",
    editor = "Demner-fushman, Dina  and
      Ananiadou, Sophia  and
      Cohen, Kevin",
    booktitle = "The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2023.bionlp-1.62/",
    doi = "10.18653/v1/2023.bionlp-1.62",
    pages = "586--591",
    abstract = "This study describes the model design of the NCUEE-NLP system for BioLaySumm Task 2 at the BioNLP 2023 workshop. We separately fine-tune pretrained PRIMERA models to independently generate technical abstracts and lay summaries of biomedical articles. A total of seven evaluation metrics across three criteria were used to compare system performance. Our best submission was ranked first for relevance, second for readability, and fourth for factuality, tying first for overall performance."
}