@inproceedings{liu-etal-2023-lhs712ee,
    title = "{LHS}712{EE} at {B}io{L}ay{S}umm 2023: Using {BART} and {LED} to summarize biomedical research articles",
    author = "Liu, Quancheng  and
      Ren, Xiheng  and
      Vydiswaran, V.G.Vinod",
    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://aclanthology.org/2023.bionlp-1.66",
    doi = "10.18653/v1/2023.bionlp-1.66",
    pages = "620--624",
    abstract = "As part of our participation in BioLaySumm 2023, we explored the use of large language models (LLMs) to automatically generate concise and readable summaries of biomedical research articles. We utilized pre-trained LLMs to fine-tune our summarization models on two provided datasets, and adapt them to the shared task within the constraints of training time and computational power. Our final models achieved very high relevance and factuality scores on the test set, and ranked among the top five models in the overall performance.",
}

As part of our participation in BioLaySumm 2023, we explored the use of large language models (LLMs) to automatically generate concise and readable summaries of biomedical research articles. We utilized pre-trained LLMs to fine-tune our summarization models on two provided datasets, and adapt them to the shared task within the constraints of training time and computational power. Our final models achieved very high relevance and factuality scores on the test set, and ranked among the top five models in the overall performance.
Liu, Quancheng Ren, Xiheng Vydiswaran, V.G.Vinod
LHS712EE at BioLaySumm 2023: Using BART and LED to summarize biomedical research articles