@inproceedings{navarro-etal-2022-shot,
    title = "Few-shot fine-tuning {SOTA} summarization models for medical dialogues",
    author = "Navarro, David Fraile  and
      Dras, Mark  and
      Berkovsky, Shlomo",
    editor = "Ippolito, Daphne  and
      Li, Liunian Harold  and
      Pacheco, Maria Leonor  and
      Chen, Danqi  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop",
    month = jul,
    year = "2022",
    address = "Hybrid: Seattle, Washington + Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2022.naacl-srw.32/",
    doi = "10.18653/v1/2022.naacl-srw.32",
    pages = "254--266",
    abstract = "Abstractive summarization of medical dialogues presents a challenge for standard training approaches, given the paucity of suitable datasets. We explore the performance of state-of-the-art models with zero-shot and few-shot learning strategies and measure the impact of pretraining with general domain and dialogue-specific text on the summarization performance."
}