@inproceedings{kondadadi-etal-2021-optum,
    title = "Optum at {MEDIQA} 2021: Abstractive Summarization of Radiology Reports using simple {BART} Finetuning",
    author = "Kondadadi, Ravi  and
      Manchanda, Sahil  and
      Ngo, Jason  and
      McCormack, Ronan",
    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-1.32/",
    doi = "10.18653/v1/2021.bionlp-1.32",
    pages = "280--284",
    abstract = "This paper describes experiments undertaken and their results as part of the BioNLP MEDIQA 2021 challenge. We participated in Task 3: Radiology Report Summarization. Multiple runs were submitted for evaluation, from solutions leveraging transfer learning from pre-trained transformer models, which were then fine tuned on a subset of MIMIC-CXR, for abstractive report summarization. The task was evaluated using ROUGE and our best performing system obtained a ROUGE-2 score of 0.392."
}