@inproceedings{guhr-etal-2025-rethinking,
    title = "Rethinking Scene Segmentation. Advancing Automated Detection of Scene Changes in Literary Texts",
    author = "Guhr, Svenja  and
      Mao, Huijun  and
      Lin, Fengyi",
    editor = "Kazantseva, Anna  and
      Szpakowicz, Stan  and
      Degaetano-Ortlieb, Stefania  and
      Bizzoni, Yuri  and
      Pagel, Janis",
    booktitle = "Proceedings of the 9th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2025)",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.latechclfl-1.8/",
    pages = "79--86",
    ISBN = "979-8-89176-241-1",
    abstract = "Automated scene segmentation is an ongoing challenge in computational literary studies (CLS) to approach literary texts by analyzing comparable units. In this paper, we present our approach (work in progress) to text segmentation using a classifier that identifies the position of a scene change in English-language fiction. By manually annotating novels from a 20th-century US-English romance fiction corpus, we prepared training data for fine-tuning transformer models, yielding promising preliminary results for improving automated text segmentation in CLS."
}