Fabula Entropy Indexing: Objective Measures of Story Coherence
This addresses the need for better evaluation metrics in story generation research, though it is incremental as it builds on existing coherence assessment methods.
The authors tackled the problem of lacking objective measures for story coherence in automated story generation by introducing Fabula Entropy Indexing (FEI), which uses human agreement on true/false questions to assess coherence, and showed it reliably measures coherence in controlled studies with corrupted stories.
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.