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Plan-And-Write: Towards Better Automatic Storytelling

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Automatic storytelling is challenging since it requires generating long, coherent natural language to describes a sensible sequence of events. Despite considerable efforts on automatic story generation in the past, prior work either is restricted in plot planning, or can only generate stories in a narrow domain. In this paper, we explore open-domain story generation that writes stories given a title (topic) as input. We propose a plan-and-write hierarchical generation framework that first plans a storyline, and then generates a story based on the storyline. We compare two planning strategies. The dynamic schema interweaves story planning and its surface realization in text, while the static schema plans out the entire storyline before generating stories. Experiments show that with explicit storyline planning, the generated stories are more diverse, coherent, and on topic than those generated without creating a full plan, according to both automatic and human evaluations.

Lili Yao, Nanyun Peng, Ralph Weischedel, Kevin Knight, Dongyan Zhao, Rui Yan• 2018

Related benchmarks

TaskDatasetResultRank
Story Ending GenerationROCStories (test)
BLEU-124.4
43
Commonsense Story GenerationCommonsense (C.S.) Story Generation (test)
BLEU-130.8
14
Story Generation Diversity AnalysisSpeculative Model Card Task
Distinct-2 Score5.837
12
Story GenerationStory Generation Evaluation Set
Creativity65.25
12
Story Ending GenerationStory Ending Generation (test)
BLEU-124.4
6
Harm Subtype Distribution AnalysisConsumer Health Harm Study (Control)
Alienating Social Groups Distribution0.00e+0
1
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