Towards End-to-End Natural Language Story Generation Systems

Abstract

Storytelling and story generation systems usually require knowledge about the story world to be encoded in some form of knowledge representation formalism, a notoriously time-consuming task requiring expertise in storytelling and knowledge engineering. In order to alleviate this authorial bottleneck, in this paper we propose an end-to-end computational narrative system that automatically extracts the necessary domain knowledge from corpus of stories written in natural language and then uses such domain knowledge to generate new stories. Specifically, we employ narrative information extraction techniques that can automatically extract structured representations from stories and feed those representations to an analogy-based story generation system. We present the structures we used to connect two existing computational narrative systems and report our experiments using a dataset of Russian fairy tales. Specifically we look at the perceived quality of the final natural language being generated and how errors in the pipeline affect the output.