@inproceedings{he-etal-2021-damo,
    title = "damo{\_}nlp at {MEDIQA} 2021: Knowledge-based Preprocessing and Coverage-oriented Reranking for Medical Question Summarization",
    author = "He, Yifan  and
      Chen, Mosha  and
      Huang, Songfang",
    editor = "Demner-Fushman, Dina  and
      Cohen, Kevin Bretonnel  and
      Ananiadou, Sophia  and
      Tsujii, Junichi",
    booktitle = "Proceedings of the 20th Workshop on Biomedical Language Processing",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.bionlp-1.12/",
    doi = "10.18653/v1/2021.bionlp-1.12",
    pages = "112--118",
    abstract = "Medical question summarization is an important but difficult task, where the input is often complex and erroneous while annotated data is expensive to acquire. We report our participation in the MEDIQA 2021 question summarization task in which we are required to address these challenges. We start from pre-trained conditional generative language models, use knowledge bases to help correct input errors, and rerank single system outputs to boost coverage. Experimental results show significant improvement in string-based metrics."
}