Abstract
Evaluating the extent to which computer-produced stories are structured like human-invented narratives can be an important component of the quality of a story plot. In this paper, we report on an empirical experiment in which human subjects have invented short plots in a constrained scenario. The stories were annotated according to features commonly found in existing automatic story generators. The annotation was designed to measure the proportion and relations of story components that should be used in automatic computational systems for matching human behaviour. Results suggest that there are relatively common patterns that can be used as input data for identifying similarity to human-invented stories in automatic storytelling systems. The found patterns are in line with narratological models, and the results provide numerical quantification and layout of story components. The proposed method of story analysis is tested over two additional sources, the ROCStories corpus and stories generated by automated storytellers, to illustrate the valuable insights that may be derived from them.
Highlights
Creating story generation systems is a complex task
The analysis provided in Relevant insights from existing story generation systems” reveals that, implicitly or explicitly, all reviewed story generation systems use a form of causality as an important component for connecting the plot events
The agreement regarding the annotation of story components as story actions or story descriptions was measured by computing the number of story components that were annotated under the same category
Summary
The number of features that can play a role in the generation or the evaluation of automatically generated stories is large, as evidenced by the heterogeneity of systems described in the literature. New Generation Computing (2020) 38:635–671 features include aspects related with the story world like emotions, characters, locations or intentions, and structural aspects like length or narrative arc. Some of these features need explicit or implicit values for the generation, as setting the appropriate length, the number of characters, or the amount of descriptions that the story needs. The parameters for the story features can change depending on the kind of the story, author, and context
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