Detecting Scenes in Fiction: A new Segmentation Task (2021)

Abstract: This paper introduces the novel task of scene segmentation on narrative texts and provides an annotated corpus, a discussion of the linguistic and narrative properties of the task and baseline experiments towards automatic solutions. A scene here is a segment of the text where time and discourse time are more or less equal, the narration focuses on one action and location and character constellations stay the same. The corpus we describe consists of German-language dime novels (550k tokens) that have been annotated in parallel, achieving an inter-annotator agreement of gamma = 0.7. Baseline experiments using BERT achieve an F1 score of 24%, showing that the task is very challenging. An automatic scene segmentation paves the way towards processing longer narrative texts like tales or novels by breaking them down into smaller, coherent and meaningful parts, which is an important stepping stone towards the reconstruction of plot in Computational Literary Studies but also can serve to improve tasks like coreference resolution.

## introduction text segmentation is a long standing issue in the area of natural language processing (nlp) encompassing different tasks like segmenting a text into sentences or finding the boundaries between different topics. in this paper, we introduce the task of scene segmentation. a scene can be understood as a segment of a text where the story time and the discourse time are more or less equal, the narration focuses on one action and space and character constellations stay the same. scenes can be found predominately in narrative texts like novels or biographies, which can be understood as a sequence of segments, where some of the segments are scenes and others are not. scene segmentation is of great interest for the high-level analysis of longer texts, for example the reconstruction of plot, but also for many areas of nlp that deal with longer narrative texts, since even modern methods struggle with processing text longer than a couple of sentences or paragraphs. as an example, the memory requirements of state of the art coreference resolution models scale with o(n 4 ) (input length n) @xcite and their performance deteriorates on longer texts. therefore, it is very helpful to break down the texts into smaller pieces where the character constellation remains the same, enabling us to perform coreference resolution within a scene and then match the characters identified across multiple scenes. additionally, scene segmentation can also be used to facilitate the summarization of long texts: since @xcite has shown that parts of a human written summary correspond well to their similar notion of coherent segments in the original text, it is reasonable to assume that segmenting a text into scenes and then summarizing these scenes is a promising way towards the summarization of long texts. finally, the number and length of scenes in a text defines a kind of "narrative rhythm", which we briefly analyze in section 4.3. this rhythm might serve as a characteristic of an author, or be used as a metric for recommending books to readers. while the objective of scene segmentation is structurally similar to topic segmentation, there are some important differences: scenes are defined as narrative units, where each unit has a coherent and stable structure in respect to time, place, character constellation and plot. narrated time and narrative time in a scene @xcite are more or less equal, which can be seen, for example, in the rendition of verbal communication as direct speech. scenes in fiction are thus not based only on the topic covered in the narrative. for example, a new scene may cover the same topic as the previous one, but take place in a different location or with a different set of characters. thus, commonly used segmentation algorithms fail at our task. even a fine-tuned bert-based model does not perform well, as we show in section 5.1. we see this as a sign that scene segmentation requires a large amount of natural language understanding and can, along with other tasks related to an indepth analysis of narrative structures, serve as a challenge and benchmark for future nlp models. our contributions in this paper are as follows: we present and publish a new data set of german fictional texts annotated with scenes and provide an extensive discussion of the guidelines used in the annotation process (which are also included in the release). additionally, we show that established baselines for text segmentation fail to capture the notion of a narrative scene, necessitating the development of new methods for this task. our main goal is to introduce the task of scene segmentation and provide resources as well as guidelines to enable research towards this task, which will in turn improve the possibility of processing long, narrative texts in the future. ## related work other segmentation tasks have been discussed in nlp for a while, mostly with the goal of identifying regions of news or other non-fictional texts discussing certain topics. the task of topic segmentation is then to identify points in the text in which the topic under discussion changes. early work to this end uses similarity of adjacent text segments (such as sentences or paragraphs) with a manually designed similarity metric in order to produce the resulting segments. one of the most well known systems of this manner is texttiling @xcite , which was applied to science magazines. similarity based on common words @xcite was superseded with the introduction of latent dirichlet allocation @xcite , which allowed to segment the text into coherent text snippets with similar topic distributions @xcite . this procedure was extended by the integration of entity coherence @xcite and @xcite have used it on (very short) narrative texts in an attempt to extract scripts. recently, many approaches making use of neural architectures deal with the detection and classification of local coherence (e. g. @xcite @xcite , which is an important step for a text summarization of high quality @xcite . text segmentation using neural architectures was conducted on chinese texts and it was shown that recurrent neural networks are able to predict the coherence of subsequent paragraphs with an accuracy of more than 80 % @xcite . @xcite compare three bert based architectures for segmentation tasks: cross-segment bert following the nsp pretraining-task and fine-tuned on segmentation, a bi-lstm on top of bert to keep track of larger context and an adaption of a hierarchical bert network @xcite . some work has been done on segmenting narrative texts, but aiming at identifying topical segments -which, as we have pointed out above, is different from scene segmentation. with a set of handcrafted features, @xcite achieve a windowdiff score @xcite of about 0.5, evaluated on two novels. @xcite have annotated the novel moonstone with topical segments, and presented a model to create a hierarchy of topic segments. they report about 0.3 windowdiff score. most closely related to our task are the papers by reiter (2015), which documents a number of annotation experiments, and @xcite , which presents lexical cohesiveness based on the semantic network paradigme @xcite as an indicator for scene boundaries and evaluates their approach qualitatively on a single novel. however, neither of them provide annotation guidelines, annotated data or a formal definition of the task. a related area of research is discourse segmentation, where the goal is also to find segments that are not necessarily defined by topic, and are also assigned labels in addition to the segmentation. there are annotated news corpora in this area featuring fine-grained discourse relations between relatively small text spans @xcite . although larger structures have been discussed in literature @xcite , no annotated corpora have been released. ## task: scene segmentation in this section we will first present a description of the task placing it in narratological tradition, and then we will describe it more formally as an atypical instance of a segmentation task. ## conclusion this paper describes the task of detecting scenes in narrative texts and introduces a corpus annotated for this task. the corpus consists of a number of german dime novels annotated according to guidelines describing the specifics of the task in detail. the inter-annotator agreement indicates that the task is challenging, but feasible for humans. as the analysis of the corpus shows, the information about character constellation, time, space and action is informative, but only an integral understanding of the text makes it possible to fully solve the task. thus, apart from the many applications of a scene segmentation itself, it also provides an interesting challenge for natural language processing: due to the high level of natural language understanding required, it will likely necessitate the development of novel approaches to be solved satisfactorily.