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Of Human Criteria and Automatic Metrics: A Benchmark of the Evaluation of Story Generation

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arxiv 2208.11646 v4 pith:G6TQ2TVP submitted 2022-08-24 cs.CL

Cyril Chhun , Pierre Colombo , Chloé Clavel , Fabian M. Suchanek This is my paper · ORCID
classification cs.CL
keywords automaticcriteriaevaluationhumanmetricsallowsanalysisgeneration
verification ladder T0 review T1 audit T2 compute T3 formal
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Research on Automatic Story Generation (ASG) relies heavily on human and automatic evaluation. However, there is no consensus on which human evaluation criteria to use, and no analysis of how well automatic criteria correlate with them. In this paper, we propose to re-evaluate ASG evaluation. We introduce a set of 6 orthogonal and comprehensive human criteria, carefully motivated by the social sciences literature. We also present HANNA, an annotated dataset of 1,056 stories produced by 10 different ASG systems. HANNA allows us to quantitatively evaluate the correlations of 72 automatic metrics with human criteria. Our analysis highlights the weaknesses of current metrics for ASG and allows us to formulate practical recommendations for ASG evaluation.

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  1. Can LLMs Generate Good Stories? Insights and Challenges from a Narrative Planning Perspective

    cs.CL 2025-06 conditional novelty 6.0 of 10

    A new automatically verified benchmark shows GPT-4 tier LLMs can do small-scale causal story planning, but character intentionality and dramatic conflict remain hard except for reasoning models like o1.

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