Story Generation with Crowdsourced Plot Graphs

Authors

  • Boyang Li Georgia Institute of Technology
  • Stephen Lee-Urban Georgia Institute of Technology
  • George Johnston Georgia Institute of Technology
  • Mark Riedl Georgia Institute of Technology

DOI:

https://doi.org/10.1609/aaai.v27i1.8649

Keywords:

Story Generation, Crowdsourcing

Abstract

Story generation is the problem of automatically selecting a sequence of events that meet a set of criteria and can be told as a story. Story generation is knowledge-intensive; traditional story generators rely on a priori defined domain models about fictional worlds, including characters, places, and actions that can be performed. Manually authoring the domain models is costly and thus not scalable. We present a novel class of story generation system that can generate stories in an unknown domain. Our system (a) automatically learns a domain model by crowdsourcing a corpus of narrative examples and (b) generates stories by sampling from the space defined by the domain model. A large-scale evaluation shows that stories generated by our system for a previously unknown topic are comparable in quality to simple stories authored by untrained humans

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Published

2013-06-30

How to Cite

Li, B., Lee-Urban, S., Johnston, G., & Riedl, M. (2013). Story Generation with Crowdsourced Plot Graphs. Proceedings of the AAAI Conference on Artificial Intelligence, 27(1), 598-604. https://doi.org/10.1609/aaai.v27i1.8649