A model of suspense for narrative generation

Abstract

Most work on automatic generation of narratives, and more specifically suspenseful narrative, has focused on detailed domain-specific modelling of character psychology and plot structure. Recent work in computational linguistics on the automatic learning of narrative schemas suggests an alternative approach that exploits such schemas as a starting point for modelling and measuring suspense. We propose a domain-independent model for tracking suspense in a story which can be used to predict the audience's suspense response on a sentence-by-sentence basis at the content determination stage of narrative generation. The model lends itself as the theoretical foundation for a suspense module that is compatible with alternative narrative generation theories. The proposal is evaluated by human judges' normalised average scores correlate strongly with predicted values.

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