Inproceedings,
Assessing the State of the Art in Scene Segmentation
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), page 9922--9941. Albuquerque, New Mexico, Association for Computational Linguistics, (April 2025)
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.
