@inproceedings{barrow-etal-2020-joint,
    title = "A Joint Model for Document Segmentation and Segment Labeling",
    author = "Barrow, Joe  and
      Jain, Rajiv  and
      Morariu, Vlad  and
      Manjunatha, Varun  and
      Oard, Douglas  and
      Resnik, Philip",
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.acl-main.29",
    doi = "10.18653/v1/2020.acl-main.29",
    pages = "313--322",
    abstract = "Text segmentation aims to uncover latent structure by dividing text from a document into coherent sections. Where previous work on text segmentation considers the tasks of document segmentation and segment labeling separately, we show that the tasks contain complementary information and are best addressed jointly. We introduce Segment Pooling LSTM (S-LSTM), which is capable of jointly segmenting a document and labeling segments. In support of joint training, we develop a method for teaching the model to recover from errors by aligning the predicted and ground truth segments. We show that S-LSTM reduces segmentation error by 30{\%} on average, while also improving segment labeling.",
}