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        <title>Readability Reconsidered A Cross-Dataset Analysis of Reference-Free Metrics</title>
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    <abstract>Automatic readability assessment plays a key role in ensuring effective communication between humans and language models. Despite significant progress the field is hindered by inconsistent definitions of readability and measurements that rely on surface-level text properties. In this work we investigate the factors shaping human perceptions of readability through the analysis of 1.2k judgments finding that beyond surface-level cues information content and topic strongly shape text comprehensibility. Furthermore we evaluate 15 popular readability metrics across 5 datasets contrasting them with 5 more nuanced model-based metrics. Our results show that four model-based metrics consistently place among the top 4 in rank correlations with human judgments while the best performing traditional metric achieves an average rank of 7.8. These findings highlight a mismatch between current readability metrics and human perceptions pointing to model-based approaches as a more promising direction.</abstract>
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%0 Conference Proceedings
%T Readability Reconsidered A Cross-Dataset Analysis of Reference-Free Metrics
%A Belem, Catarina
%A Glenn, Parker
%A Samuel, Alfy
%A Kumar, Anoop
%A Liu, Daben
%Y Shardlow, Matthew
%Y Alva-Manchego, Fernando
%Y North, Kai
%Y Stodden, Regina
%Y Saggion, Horacio
%Y Khallaf, Nouran
%Y Hayakawa, Akio
%S Proceedings of the Fourth Workshop on Text Simplification, Accessibility and Readability (TSAR 2025)
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-176-6
%F belem-etal-2025-readability
%X Automatic readability assessment plays a key role in ensuring effective communication between humans and language models. Despite significant progress the field is hindered by inconsistent definitions of readability and measurements that rely on surface-level text properties. In this work we investigate the factors shaping human perceptions of readability through the analysis of 1.2k judgments finding that beyond surface-level cues information content and topic strongly shape text comprehensibility. Furthermore we evaluate 15 popular readability metrics across 5 datasets contrasting them with 5 more nuanced model-based metrics. Our results show that four model-based metrics consistently place among the top 4 in rank correlations with human judgments while the best performing traditional metric achieves an average rank of 7.8. These findings highlight a mismatch between current readability metrics and human perceptions pointing to model-based approaches as a more promising direction.
%R 10.18653/v1/2025.tsar-1.4
%U https://aclanthology.org/2025.tsar-1.4/
%U https://doi.org/10.18653/v1/2025.tsar-1.4
%P 47-69

Markdown (Informal)

[Readability Reconsidered A Cross-Dataset Analysis of Reference-Free Metrics](https://aclanthology.org/2025.tsar-1.4/) (Belem et al., TSAR 2025)

• Readability Reconsidered A Cross-Dataset Analysis of Reference-Free Metrics (Belem et al., TSAR 2025)

ACL

• Catarina Belem, Parker Glenn, Alfy Samuel, Anoop Kumar, and Daben Liu. 2025. Readability Reconsidered A Cross-Dataset Analysis of Reference-Free Metrics. In Proceedings of the Fourth Workshop on Text Simplification, Accessibility and Readability (TSAR 2025), pages 47–69, Suzhou, China. Association for Computational Linguistics.