Identifying Sensible Lexical Relations in Generated Stories

Abstract

As with many text generation tasks, the focus of recent progress on story generation has been in producing texts that are perceived to ``make sense'' as a whole. There are few automated metrics that address this dimension of story quality even on a shallow lexical level. To initiate investigation into such metrics, we apply a simple approach to identifying word relations that contribute to the `narrative sense' of a story. We use this approach to comparatively analyze the output of a few notable story generation systems in terms of these relations. We characterize differences in the distributions of relations according to their strength within each story.

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Reproductions