  Narrative story generation is a challenging problem because it demands the
generated sentences with tight semantic connections, which has not been well
studied by most existing generative models. To address this problem, we propose
a skeleton-based model to promote the coherence of generated stories. Different
from traditional models that generate a complete sentence at a stroke, the
proposed model first generates the most critical phrases, called skeleton, and
then expands the skeleton to a complete and fluent sentence. The skeleton is
not manually defined, but learned by a reinforcement learning method. Compared
to the state-of-the-art models, our skeleton-based model can generate
significantly more coherent text according to human evaluation and automatic
evaluation. The G-score is improved by 20.1% in the human evaluation. The code
is available at https://github.com/lancopku/Skeleton-Based-Generation-Model
