Story Realization: Expanding Plot Events into Sentences


  • Prithviraj Ammanabrolu Georgia Institute of Technology
  • Ethan Tien Georgia Institute of Technology
  • Wesley Cheung Georgia Institute of Technology
  • Zhaochen Luo Georgia Institute of Technology
  • William Ma Georgia Institute of Technology
  • Lara J. Martin Georgia Institute of Technology
  • Mark O. Riedl Georgia Institute of Technology



Neural network based approaches to automated story plot generation attempt to learn how to generate novel plots from a corpus of natural language plot summaries. Prior work has shown that a semantic abstraction of sentences called events improves neural plot generation and and allows one to decompose the problem into: (1) the generation of a sequence of events (event-to-event) and (2) the transformation of these events into natural language sentences (event-to-sentence). However, typical neural language generation approaches to event-to-sentence can ignore the event details and produce grammatically-correct but semantically-unrelated sentences. We present an ensemble-based model that generates natural language guided by events. We provide results—including a human subjects study—for a full end-to-end automated story generation system showing that our method generates more coherent and plausible stories than baseline approaches 1.




How to Cite

Ammanabrolu, P., Tien, E., Cheung, W., Luo, Z., Ma, W., Martin, L. J., & Riedl, M. O. (2020). Story Realization: Expanding Plot Events into Sentences. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 7375-7382.



AAAI Technical Track: Natural Language Processing