  Long story generation (LSG) is one of the coveted goals in natural language
processing. Different from most text generation tasks, LSG requires to output a
long story of rich content based on a much shorter text input, and often
suffers from information sparsity. In this paper, we propose \emph{TopNet} to
alleviate this problem, by leveraging the recent advances in neural topic
modeling to obtain high-quality skeleton words to complement the short input.
In particular, instead of directly generating a story, we first learn to map
the short text input to a low-dimensional topic distribution (which is
pre-assigned by a topic model). Based on this latent topic distribution, we can
use the reconstruction decoder of the topic model to sample a sequence of
inter-related words as a skeleton for the story. Experiments on two benchmark
datasets show that our proposed framework is highly effective in skeleton word
selection and significantly outperforms the state-of-the-art models in both
automatic evaluation and human evaluation.
