@inproceedings{zehe-etal-2025-assessing,
    title = "Assessing the State of the Art in Scene Segmentation",
    author = "Zehe, Albin  and
      Fischer, Elisabeth  and
      Hotho, Andreas",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-long.500/",
    pages = "9922--9941",
    ISBN = "979-8-89176-189-6",
    abstract = "The detection of scenes in literary texts is a recently introduced segmentation task in computational literary studies. Its goal is to partition a fictional text into segments that are coherent across the dimensions time, space, action and character constellation. This task is very challenging for automatic methods, since it requires a high-level understanding of the text. In this paper, we provide a thorough analysis of the State of the Art and challenges in this task, identifying and solving a problem in the training procedure for previous approaches, analysing the generalisation capabilities of the models and comparing the BERT-based SotA to current Llama models, as well as providing an analysis of what causes errors in the models. Our change in training procedure provides a significant increase in performance. We find that Llama-based models are more robust to different types of texts, while their overall performance is slightly worse than that of BERT-based models."
}