Modeling Event Salience in Narratives via Barthes’ Cardinal Functions (2020)

Abstract: Events in a narrative differ in salience: some are more important to the story than others. Estimating event salience is useful for tasks such as story generation, and as a tool for text analysis in narratology and folkloristics. To compute event salience without any annotations, we adopt Barthes’ definition of event salience and propose several unsupervised methods that require only a pre-trained language model. Evaluating the proposed methods on folktales with event salience annotation, we show that the proposed methods outperform baseline methods and find fine-tuning a language model on narrative texts is a key factor in improving the proposed methods.

## introduction narratives (e.g., folktales, literary short stories) are the representations of a series of events @xcite . events, the essential components of narratives, differ in salience: some are more important to the story than others. taking cinderella as an example, the prince falls in love with cinderella is a salient event; however, cinderella draws water from a well is not. estimating event salience is a fundamental task in analyzing and processing narratives, ranging from narrative analysis to automatic story generation @xcite @xcite . this study aims to estimate event salience in an unsupervised manner. manually annotating event salience is prohibitively costly because it requires annotators to deeply understand the notion of event salience in narratology @xcite . in fact, despite a long history of research, very few narrative corpora are annotated with event salience. thus, it is crucial to develop a method for estimating event salience that does not rely on event salience-annotated corpora. in order to estimate event salience without annotated data, we adopt the definition of cardinal functions (cfs) introduced by @xcite , the successor of proppian function 1 @xcite , as follows: ## related work numerous studies on the salience of text units (e.g., word, sentence) can be related to our work. here, we review two particularly relevant topics. first, the deletion test @xcite aims to identify salient discourse segments in rhetorical structure theory @xcite . in the deletion test, annotators check how much discourse coherence is reduced by removing the discourse unit of interest. notably, @xcite and @xcite use essentially the same idea, to "remove the textual unit of interest, and see how the whole structure changes," although the task is quite different. second, extractive summarization is a task of identifying salient sentences in documents, which is formally very similar to the task of our work. despite various existing approaches for extractive summarization @xcite , it is the open problem whether these methods can be directly applied to narrative texts. extractive summarization conventionally focuses on domains with rigid structures, such as news articles or scientific papers, while narrative texts do not have such rigid structures @xcite . in the context of narrative processing in nlp, several methods have been proposed to identify some kinds of salient events: suspenseful events in entertainment stories @xcite , turning points in a movie script @xcite , and reportable events in personal narratives @xcite . in contrast to studies focusing on a specific type of narrative (e.g., movie scripts, personal narratives), our method is potentially applicable to any type of narrative because barthes' cfs is not a concept specific to those particular kinds of narratives and because our methods require only a pre-trained language model. 3 estimating event salience ## discussion and future work one promising direction for improving our proposed methods is to improve the narrative coherence evaluator. for more accurate coherence evaluation, the coherence evaluator needs to have world knowledge and common sense reasoning skills. imagine the story of cinderella. to be able to identify that the absence of event the prince falls in love with cinderella leads to coherence reduction, an ideal coherence evaluator needs to recognize that this event has a strong causal relation (in this case, precondition) with the next event cinderella marries the prince. recently, several techniques have been proposed to provide language models with more world knowledge @xcite and to enhance the common sense reasoning skills of language models @xcite . evaluating the coherence of a narrative using these lms can potentially improve our proposed methods. ## conclusions inspired by the barthes' definition of cardinal functions in narratology, we have proposed methods to estimate event salience in a narrative in an unsupervised manner using an lm. in our proposed methods, we have removed events from a narrative text and have estimated event salience by comparing the coherence score of the original narrative text with that of the event-removed narrative text. experiments on a folktales dataset have demonstrated that the proposed methods outperformed baseline methods and fine-tuning the lm on a narrative text is an effective way to improve the proposed methods.