@inproceedings{liu-etal-2023-deakinnlp,
    title = "{D}eakin{NLP} at {P}rob{S}um 2023: Clinical Progress Note Summarization with Rules and Language {M}odels{C}linical Progress Note Summarization with Rules and Languague Models",
    author = "Liu, Ming  and
      Zhang, Dan  and
      Tan, Weicong  and
      Zhang, He",
    editor = "Demner-fushman, Dina  and
      Ananiadou, Sophia  and
      Cohen, Kevin",
    booktitle = "Proceedings of 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/ingest-emnlp/2023.bionlp-1.47/",
    doi = "10.18653/v1/2023.bionlp-1.47",
    pages = "491--496",
    abstract = "This paper summarizes two approaches developed for BioNLP2023 workshop task 1A: clinical problem list summarization. We develop two types of methods with either rules or pre-trained language models. In the rule-based summarization model, we leverage UMLS (Unified Medical Language System) and a negation detector to extract text spans to represent the summary. We also fine tune three pre-trained language models (BART, T5 and GPT2) to generate the summaries. Experiment results show the rule based system returns extractive summaries but lower ROUGE-L score (0.043), while the fine tuned T5 returns a higher ROUGE-L score (0.208)."
}