A model of suspense for narrative generation
Richard Doust, Paul Piwek · 2017
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 on the automatic learning of narrative schemas suggests an alternative approach that exploits such schemas 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.