  Story generation is a task that aims to automatically produce multiple
sentences to make up a meaningful story. This task is challenging because it
requires high-level understanding of semantic meaning of sentences and
causality of story events. Naive sequence-to-sequence models generally fail to
acquire such knowledge, as the logical correctness can hardly be guaranteed in
a text generation model without the strategic planning. In this paper, we focus
on planning a sequence of events assisted by event graphs, and use the events
to guide the generator. Instead of using a sequence-to-sequence model to output
a storyline as in some existing works, we propose to generate an event sequence
by walking on an event graph. The event graphs are built automatically based on
the corpus. To evaluate the proposed approach, we conduct human evaluation both
on event planning and story generation. Based on large-scale human annotation
results, our proposed approach is shown to produce more logically correct event
sequences and stories.
