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        <title>Fabula Entropy Indexing: Objective Measures of Story Coherence</title>
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        <namePart type="given">Louis</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2021.nuse-1.9</identifier>
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%0 Conference Proceedings
%T Fabula Entropy Indexing: Objective Measures of Story Coherence
%A Castricato, Louis
%A Frazier, Spencer
%A Balloch, Jonathan
%A Riedl, Mark
%Y Akoury, Nader
%Y Brahman, Faeze
%Y Chaturvedi, Snigdha
%Y Clark, Elizabeth
%Y Iyyer, Mohit
%Y Martin, Lara J.
%S Proceedings of the Third Workshop on Narrative Understanding
%D 2021
%8 June
%I Association for Computational Linguistics
%C Virtual
%F castricato-etal-2021-fabula
%X 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.
%R 10.18653/v1/2021.nuse-1.9
%U https://aclanthology.org/2021.nuse-1.9
%U https://doi.org/10.18653/v1/2021.nuse-1.9
%P 84-94

Markdown (Informal)

[Fabula Entropy Indexing: Objective Measures of Story Coherence](https://aclanthology.org/2021.nuse-1.9) (Castricato et al., NUSE-WNU 2021)

• Fabula Entropy Indexing: Objective Measures of Story Coherence (Castricato et al., NUSE-WNU 2021)

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.