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Story generation is a task that aims to automatically produce multiplesentences to make up a meaningful story. This task is challenging because itrequires high-level understanding of semantic meaning of sentences andcausality of story events. Naive sequence-to-sequence models generally fail toacquire such knowledge, as the logical correctness can hardly be guaranteed ina text generation model without the strategic planning. In this paper, we focuson planning a sequence of events assisted by event graphs, and use the eventsto guide the generator. Instead of using a sequence-to-sequence model to outputa storyline as in some existing works, we propose to generate an event sequenceby walking on an event graph. The event graphs are built automatically based onthe corpus. To evaluate the proposed approach, we conduct human evaluation bothon event planning and story generation. Based on large-scale human annotationresults, our proposed approach is shown to produce more logically correct eventsequences and stories.

GraphPlan: Story Generation by Planning with Event Graph