Plan-and-Write: Towards Better Automatic Storytelling


  • Lili Yao Tencent
  • Nanyun Peng University of Southern California
  • Ralph Weischedel University of Southern California
  • Kevin Knight University of Southern California
  • Dongyan Zhao Peking University
  • Rui Yan Peking University



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.




How to Cite

Yao, L., Peng, N., Weischedel, R., Knight, K., Zhao, D., & Yan, R. (2019). Plan-and-Write: Towards Better Automatic Storytelling. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 7378-7385.



AAAI Technical Track: Natural Language Processing