@inproceedings{ferracane-etal-2019-news,
    title = "From News to Medical: Cross-domain Discourse Segmentation",
    author = "Ferracane, Elisa  and
      Page, Titan  and
      Li, Junyi Jessy  and
      Erk, Katrin",
    editor = "Zeldes, Amir  and
      Das, Debopam  and
      Galani, Erick Maziero  and
      Antonio, Juliano Desiderato  and
      Iruskieta, Mikel",
    booktitle = "Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019",
    month = jun,
    year = "2019",
    address = "Minneapolis, MN",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W19-2704/",
    doi = "10.18653/v1/W19-2704",
    pages = "22--29",
    abstract = "The first step in discourse analysis involves dividing a text into segments. We annotate the first high-quality small-scale medical corpus in English with discourse segments and analyze how well news-trained segmenters perform on this domain. While we expectedly find a drop in performance, the nature of the segmentation errors suggests some problems can be addressed earlier in the pipeline, while others would require expanding the corpus to a trainable size to learn the nuances of the medical domain."
}