Rethinking Scene Segmentation. Advancing Automated Detection of Scene Changes in Literary Texts (2025)

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.

## introduction segmenting literary prose into meaningful units, such as events, plots, or scenes, opens up new possibilities for comparative analysis by focusing on smaller units rather than entire texts. however, automating this process remains a significant challenge in cls. while many computational approaches depend on pre-segmented texts due to input size limitations, standardized methods for segmentation are still lacking. as a result, heuristic approaches, such as dividing texts into equalsized units or relying on chapter boundaries, are often used -even though chapter divisions typically reflect editorial choices rather than coherent narrative structures, and especially popular fiction and serialized novels often play with cliff hangers that extend a key action beyond chapter boundaries @xcite @xcite . drawing from their established use in dramatic texts and film studies, scenes have emerged as useful units for segmenting literary prose. defined by consistency in time, place, and characters, scenes "center around a particular action" @xcite . this internal coherence allows them to function as self-contained, meaningful units that can be systematically compared to other scenes within a narrative or a text corpus. for instance, consider a novel in which an initial scene takes place in a supermarket where one of the characters is depicted grocery shopping. this is followed by a new scene set in a kitchen where two characters are cooking and talking. each scene can be analyzed independently in terms of its temporal and spatial dimensions. by segmenting a text into such discrete units, we enable systematic comparative investigations of character constellations, spatial patterns, and thematic developments. for example, after identifying all the scenes that take place in a supermarket, one could compare the recurring characters in those scenes and analyze their actions in that specific space. the automation of scene annotation was first approached by @xcite , whose definition served as the basis for the shared task of scene segmentation (stss) of german prose @xcite . this initiative included the development of scene detection guidelines @xcite and the creation of german-language training datasets with manually annotated scenes to support automated methods. the most effective approach, developed by @xcite , utilized a bert-based model with weighted cross-entropy and the iob2 scheme, focusing on identifying scene boundaries rather than full segments. our goal is to make a first attempt at developing a scene recognition classifier for us-english fiction. we build on the winning team's approach in the german shared task, but use more recent language models and an approximation strategy that works by predicting scene changes that occur in six-sentence segments foot_0 . since the submission of this paper in january 2025, we have learned of an independent but comparable approach developed by @xcite . their work, focusing on german texts, extends the earlier scene segmentation project @xcite paused in 2022 after the completion and evaluation of the shared task at konvens 2021. to evaluate their inter-annotator agreement and the performance of their automation, they introduce a new metric, namely a "relaxed f1 score" @xcite , which allows a tolerance of three sentences for the detected position of a scene change in the manual and automated annotations. the authors argue that fluid scene changes, which cannot be precisely positioned in the text even by human annotators, usually occur within a window of three sentences. accordingly, the relaxed f1 score gives better scoring results that reflect the performance of the human annotators and the models @xcite . these findings are consistent with our observation that scene change transitions can span up to three sentences, which led to our decision to use a six-sentence segment approach for the prediction process. ## evaluation and error analysis for the evaluation, we compiled a test set using the final five manually annotated scenes from each of the ten romance novels in the original corpus. these last five scenes were previously excluded from the training set, resulting in a total of 50 scenes. like the training data, they were segmented into six-sentence segments (0.8 majority baseline). this approach ensured that the test set remained sufficiently similar to the data of interest, namely our us romance novel corpus, while still providing enough variation to assess the model's generalization ability. the evaluation on the unseen test set reveals that the model is more prone to overlooking a scene change than to mistakenly identifying one where none exists, as there are many more false negatives than false positives. through an examination of individual examples, we identify several factors that influence the model's predictions: 1) segment length, 2) characters and pronouns, 3) ambiguity in manual annotations. first, we find that the model is more likely to make errors when processing longer inputs. specifically, by calculating the average segment length, we observed that the biggest difference was between correct cases and false positives, indicating that the model is more likely to detect a scene change in longer segments. second, we identify character names as a key factor influencing the model's predictions, particularly in cases where errors occur. we recognize that false positive and false negative cases are governed by different aspects of character mentions. in false positive cases, the model misinterprets a continuous scene as a scene change due to the introduction of new characters, which incorrectly signals a break. conversely, in false negative cases, actual scene breaks are mistaken for continuity because the model recognizes recurring names or pronouns across scenes, leading to incorrect predictions. finally, we also identify a third group of errors where the reasoning ## conclusion and outlook in conclusion, the evaluation results and the error analysis foot_5 are promising, but the current approach only approximates scene change positions within six-sentence segments. to enhance precision, we started developing a sentence-wise prediction model that identifies the first sentence of a six-sentence segment previously predicted with a high probability of bearing a scene-change. however, the task is still far from being solved and with our contribution we want to reopen the discussion on scene segmentation, and add a new perspective to the discourse on meaningful literary text segmentation for cls.