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Events in a narrative differ in salience: some are more important to thestory than others. Estimating event salience is useful for tasks such as storygeneration, and as a tool for text analysis in narratology and folkloristics.To compute event salience without any annotations, we adopt Barthes' definitionof event salience and propose several unsupervised methods that require only apre-trained language model. Evaluating the proposed methods on folktales withevent salience annotation, we show that the proposed methods outperformbaseline methods and find fine-tuning a language model on narrative texts is akey factor in improving the proposed methods.

Modeling Event Salience in Narratives via Barthes' Cardinal Functions