We propose the task of outline-conditioned story generation: given an outline
as a set of phrases that describe key characters and events to appear in a
story, the task is to generate a coherent narrative that is consistent with the
provided outline. This task is challenging as the input only provides a rough
sketch of the plot, and thus, models need to generate a story by interweaving
the key points provided in the outline. This requires the model to keep track
of the dynamic states of the latent plot, conditioning on the input outline
while generating the full story. We present PlotMachines, a neural narrative
model that learns to transform an outline into a coherent story by tracking the
dynamic plot states. In addition, we enrich PlotMachines with high-level
discourse structure so that the model can learn different writing styles
corresponding to different parts of the narrative. Comprehensive experiments
over three fiction and non-fiction datasets demonstrate that large-scale
language models, such as GPT-2 and Grover, despite their impressive generation
performance, are not sufficient in generating coherent narratives for the given
outline, and dynamic plot state tracking is important for composing narratives
with tighter, more consistent plots.

该研究提出了基于提纲的故事生成任务，通过追踪动态情节状态，将提纲转换成一篇连贯的故事的神经叙事模型可学习不同写作风格，包括大规模语言模型生成的故事一般存在不连贯的情况。