@inproceedings{park-etal-2022-leveraging,
    title = "Leveraging Non-dialogue Summaries for Dialogue Summarization",
    author = "Park, Seongmin  and
      Shin, Dongchan  and
      Lee, Jihwa",
    editor = "Dernoncourt, Franck  and
      Nguyen, Thien Huu  and
      Lai, Viet Dac  and
      Veyseh, Amir Pouran Ben  and
      Bui, Trung H.  and
      Yoon, David Seunghyun",
    booktitle = "Proceedings of the First Workshop On Transcript Understanding",
    month = oct,
    year = "2022",
    address = "Gyeongju, South Korea",
    publisher = "International Conference on Computational Linguistics",
    url = "https://aclanthology.org/2022.tu-1.1",
    pages = "1--7",
    abstract = "To mitigate the lack of diverse dialogue summarization datasets in academia, we present methods to utilize non-dialogue summarization data for enhancing dialogue summarization systems. We apply transformations to document summarization data pairs to create training data that better befit dialogue summarization. The suggested transformations also retain desirable properties of non-dialogue datasets, such as improved faithfulness to the source text. We conduct extensive experiments across both English and Korean to verify our approach. Although absolute gains in ROUGE naturally plateau as more dialogue summarization samples are introduced, utilizing non-dialogue data for training significantly improves summarization performance in zero- and few-shot settings and enhances faithfulness across all training regimes.",
}