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Readability Measures in Automatic Text Simplification: Is Simplification Quality a Coherent Construct?

Readability is a central concept in automatic text simplification (ATS), yet the two fields have largely developed in parallel, with limited cross-fertilization. While prior work has studied correlations between automatic evaluation metrics and human judgment in ATS, the correlations between these two aspects and readability measures have not received systematic attention. We address this gap by investigating to what extent readability measures align with both human judgment and automatic metrics in ATS. Using two English datasets annotated with human judgments (SimplicityDA at the sentence level and D-Wikipedia at the document level), we compute 1,066 linguistic features (covering lexical diversity, lexical sophistication, syntactic sophistication, and cohesion) and eight traditional readability formulas, and correlate them against human scores and standard ATS metrics (BLEU, SARI, BERTScore, LENS, D-SARI). Our results show that readability measures correlate poorly with both human judgment and automatic metrics across both levels. The meaning preservation criterion consistently yields the highest correlation values, while simplicity and fluency criteria remain low. We also find systematic differences between sentence-level and document-level simplification in terms of which features are most informative: type-token ratio features are predictive at the sentence level but not at the document level, while corpus-frequency features show the opposite pattern. These findings point to a broader issue: ATS lacks a shared theoretical construct for simplification quality, and the three main approaches to its assessment (human judgment, readability measures, and automatic metrics) do not consistently converge.