A Comparison of Retrieval Models for Open Domain Story Generation

In this paper we describe the architecture of an interactive story generation system where a human and computer each take turns writing sentences of an emerging narrative. Each turn begins with the user adding a sentence to the story, where the computer responds with a sentence of its own that continues what has been written so far. Rather than generating the next sentence from scratch, the computer selects the next sentence from a corpus of tens of millions of narrative sentences extracted from Internet weblogs. We compare five different retrieval methods for selecting the most appropriate sentence, and present the results of a user study to determine which of these models produces stories with the highest coherence and overall value.