Fabula Entropy Indexing: Objective Measures of Story Coherence

TL;DR

This paper introduces Fabula Entropy Indexing, a novel objective evaluation method for story coherence based on human agreement in question-answering, addressing the challenge of assessing narrative quality in automated story generation.

Contribution

It proposes two entropy-based metrics, EWC and ETC, grounded in theory, to objectively measure global and local story coherence, validated through human studies.

Findings

Entropy indices reliably measure story coherence.

Metrics distinguish between coherent and incoherent stories.

Validated on human-written and corrupted stories.

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.