  Stories are diverse and highly personalized, resulting in a large possible
output space for story generation. Existing end-to-end approaches produce
monotonous stories because they are limited to the vocabulary and knowledge in
a single training dataset. This paper introduces KG-Story, a three-stage
framework that allows the story generation model to take advantage of external
Knowledge Graphs to produce interesting stories. KG-Story distills a set of
representative words from the input prompts, enriches the word set by using
external knowledge graphs, and finally generates stories based on the enriched
word set. This distill-enrich-generate framework allows the use of external
resources not only for the enrichment phase, but also for the distillation and
generation phases. In this paper, we show the superiority of KG-Story for
visual storytelling, where the input prompt is a sequence of five photos and
the output is a short story. Per the human ranking evaluation, stories
generated by KG-Story are on average ranked better than that of the
state-of-the-art systems. Our code and output stories are available at
https://github.com/zychen423/KE-VIST.
