Fabula Entropy Indexing: Objective Measures of Story Coherence

Louis Castricato, Spencer Frazier, Jonathan Balloch, Mark Riedl


Abstract
Automated story generation remains a difficult area of research because it lacks strong objective measures. Generated stories may be linguistically sound, but in many cases suffer poor narrative coherence required for a compelling, logically-sound story. To address this, we present Fabula Entropy Indexing (FEI), an evaluation method to assess story coherence by measuring the degree to which human participants agree with each other when answering true/false questions about stories. We devise two theoretically grounded measures of reader question-answering entropy, the entropy of world coherence (EWC), and the entropy of transitional coherence (ETC), focusing on global and local coherence, respectively. We evaluate these metrics by testing them on human-written stories and comparing against the same stories that have been corrupted to introduce incoherencies. We show that in these controlled studies, our entropy indices provide a reliable objective measure of story coherence.
Anthology ID:
2021.nuse-1.9
Volume:
Proceedings of the Third Workshop on Narrative Understanding
Month:
June
Year:
2021
Address:
Virtual
Editors:
Nader Akoury, Faeze Brahman, Snigdha Chaturvedi, Elizabeth Clark, Mohit Iyyer, Lara J. Martin
Venues:
NUSE | WNU
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
84–94
Language:
URL:
https://aclanthology.org/2021.nuse-1.9
DOI:
10.18653/v1/2021.nuse-1.9
Bibkey:
Cite (ACL):
Louis Castricato, Spencer Frazier, Jonathan Balloch, and Mark Riedl. 2021. Fabula Entropy Indexing: Objective Measures of Story Coherence. In Proceedings of the Third Workshop on Narrative Understanding, pages 84–94, Virtual. Association for Computational Linguistics.
Cite (Informal):
Fabula Entropy Indexing: Objective Measures of Story Coherence (Castricato et al., NUSE-WNU 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.nuse-1.9.pdf