Flesch-Kincaid Readability Depends Only on the Topic Distribution in Long Texts under Topic Models

2026-08-24

AI Summary

研究探讨了Flesch-Kincaid可读性评分在长文本中主要受主题分布而非词汇组成的影响，通过主题模型验证了这一理论，并在两个语料库上进行了实验评估。

Abstract

Flesch Reading Ease (FRE) and the Flesch-Kincaid Grade Level (FKGL) are widely used readability scores for English computed from the same two document statistics, yet their stability on long documents need not imply invariance to lexical composition. Surprisingly, under a topic model with an explicit sentence-boundary token, both scores converge almost surely to deterministic functions of the document topic distribution through just two scalar rates: in the long-text limit, all score variation is mediated by topical composition rather than any residual readability signal. The theory covers both formulae, while the experiments evaluate FKGL. In a fixed admixture with rank[1, q, s] = 3, fibres through interior topic vectors are locally (K-3)-dimensional, whereas regular iso-score level sets are locally (K-2)-dimensional and curved. In out-of-fold evaluation on two balanced corpora, Brown and the written BNC, a topic vector inferred from one document half's content words predicts the other half's FKGL at r = 0.779 and 0.884, respectively. On Brown, adding the topic prediction to genre and mean content-word syllable count yields $ΔR^2$ = 0.002, with a confidence interval spanning zero; on the BNC, the corresponding split-half increment is 0.024, positive in four of five K = 100 fits (median 0.021). Because inferred topics may also absorb genre, register, and style, we do not interpret these results as evidence about human readability or causal effects.

Problem

• Flesch-Kincaid Grade Level
• topic distribution
• readability scores
• long texts
• topic models

Innovation

• Flesch-Kincaid Grade Level
• topic distribution
• long texts
• topic model
• readability scores