@inproceedings{wu-etal-2023-knowlab,
    title = "{K}now{L}ab at {R}ad{S}um23: comparing pre-trained language models in radiology report summarization",
    author = "Wu, Jinge  and
      Shi, Daqian  and
      Hasan, Abul  and
      Wu, Honghan",
    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-1.54",
    doi = "10.18653/v1/2023.bionlp-1.54",
    pages = "535--540",
    abstract = "This paper presents our contribution to the RadSum23 shared task organized as part of the BioNLP 2023. We compared state-of-the-art generative language models in generating high-quality summaries from radiology reports. A two-stage fine-tuning approach was introduced for utilizing knowledge learnt from different datasets. We evaluated the performance of our method using a variety of metrics, including BLEU, ROUGE, bertscore, CheXbert, and RadGraph. Our results revealed the potentials of different models in summarizing radiology reports and demonstrated the effectiveness of the two-stage fine-tuning approach. We also discussed the limitations and future directions of our work, highlighting the need for better understanding the architecture design{'}s effect and optimal way of fine-tuning accordingly in automatic clinical summarizations.",
}

This paper presents our contribution to the RadSum23 shared task organized as part of the BioNLP 2023. We compared state-of-the-art generative language models in generating high-quality summaries from radiology reports. A two-stage fine-tuning approach was introduced for utilizing knowledge learnt from different datasets. We evaluated the performance of our method using a variety of metrics, including BLEU, ROUGE, bertscore, CheXbert, and RadGraph. Our results revealed the potentials of different models in summarizing radiology reports and demonstrated the effectiveness of the two-stage fine-tuning approach. We also discussed the limitations and future directions of our work, highlighting the need for better understanding the architecture design{'}s effect and optimal way of fine-tuning accordingly in automatic clinical summarizations.
Wu, Jinge Shi, Daqian Hasan, Abul Wu, Honghan
KnowLab at RadSum23: comparing pre-trained language models in radiology report summarization