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Readability Measures and Automatic Text Simplification: In the Search of a Construct

arXiv 2511.09536 ↗First posted 12 Nov 2025cs.CLcited by 0

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Abstract (arXiv)

Readability is a key concept in the current era of abundant written information. To help making texts more readable and make information more accessible to everyone, a line of researched aims at making texts accessible for their target audience: automatic text simplification (ATS). Lately, there have been studies on the correlations between automatic evaluation metrics in ATS and human judgment. However, the correlations between those two aspects and commonly available readability measures (such as readability formulas or linguistic features) have not been the focus of as much attention. In this work, we investigate the place of readability measures in ATS by complementing the existing studies on evaluation metrics and human judgment, on English. We first discuss the relationship between ATS and research in readability, then we report a study on correlations between readability measures and human judgment, and between readability measures and ATS evaluation metrics. We identify that in general, readability measures do not correlate well with automatic metrics and human judgment. We argue that as the three different angles from which simplification can be assessed tend to exhibit rather low correlations with one another, there is a need for a clear definition of the construct in ATS.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 65 references that are in the map. First posted on arXiv 12 Nov 2025.

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19 of 65 references.

References · 19

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7 more
  1. 2019A Corpus for Automatic Readability Assessment and Text Simplification of Germancited by 18
  2. 2023Flesch or Fumble? Evaluating Readability Standard Alignment of Instruction-Tuned Language Modelscited by 17
  3. 2023Do Text Simplification Systems Preserve Meaning? A Human Evaluation via Reading Comprehensioncited by 16
  4. 2022Arabic Word-level Readability Visualization for Assisted Text Simplificationcited by 7
  5. 2024Evaluating Document Simplification: On the Importance of Separately Assessing Simplicity and Meaning Preservationcited by 5
  6. 2023Medical Text Simplification: Optimizing for Readability with Unlikelihood Training and Reranked Beam Search Decodingcited by 3
  7. 2024Readability-guided Idiom-aware Sentence Simplification (RISS) for Chinesecited by 1

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