Fabula Entropy Indexing：故事连贯性的客观测量
Fabula Entropy Indexing: Objective Measures of Story Coherence
摘要：
自动故事生成仍然是一个困难的研究领域，因为它缺乏强有力的客观措施。生成的故事可能在语言上是合理的，但在许多情况下，一个令人信服的，逻辑上合理的故事所需的叙事连贯性很差。为了解决这个问题，我们提出了Fabula熵索引（FEI），一种评估方法，通过测量人类参与者在回答关于故事的真/假问题时相互同意的程度来评估故事的连贯性。我们设计了两个理论基础的措施，读者问答熵，熵的世界连贯性（EWC），和熵的过渡连贯性（ETC），分别侧重于全球和本地的连贯性。我们通过在人类编写的故事上测试这些指标，并与被破坏以引入不连贯性的相同故事进行比较来评估这些指标。我们表明，在这些对照研究中，我们的熵指数提供了一个可靠的客观措施的故事连贯性。
英文摘要：
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.
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