Towards a Linguistic Evaluation of Narratives: A Quantitative Stylistic Framework

arXiv cs.CL / 4/22/2026

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Key Points

  • The paper introduces a quantitative framework for evaluating narrative quality by treating linguistic signals (rather than subjective story elements) as the primary indicators.
  • It proposes an automatic method that extracts 33 quantitative linguistic features spanning lexical, syntactic, and semantic categories to score and characterize narratives.
  • Experiments on a specialized corpus of 23 books (including both classic works and self-published texts) show that the system can cluster narratives and nearly perfectly separate professionally edited from self-published writing.
  • The approach is also validated against a human-annotated dataset and is reported to outperform conventional story-level evaluation metrics, supporting the value of linguistic features for narrative assessment.

Abstract

The evaluation of narrative quality remains a complex challenge, as it involves subjective factors such as plot, character development, and emotional impact. This work proposes a quantitative approach to narrative assessment by focusing on the linguistic dimension as a primary indicator of quality. The paper presents a methodology for the automatic evaluation of narrative based on the extraction of a comprehensive set of 33 quantitative linguistic features categorized into lexical, syntactic, and semantic groups. To test the model, an experiment was conducted on a specialized corpus of 23 books, including canonical masterpieces and self-published works. Through a similarity matrix, the system successfully clustered the narratives, distinguishing almost perfectly between professionally edited and self-published texts. Furthermore, the methodology was validated against a human-annotated dataset; it significantly outperforms traditional story-level evaluation metrics, demonstrating the effectiveness of quantitative linguistic features in assessing narrative quality.