Inclusive Easy-to-Read Generation for Individuals with Cognitive Impairments

François Ledoyen; Gaël Dias; Alexis Lechervy; Jeremie Pantin; Fabrice Maurel; Youssef Chahir; Elisa Gouzonnat; Mélanie Berthelot; Stanislas Moravac; Armony Altinier; Amy Khairalla

arXiv · 2510.00691

Inclusive Easy-to-Read Generation for Individuals with Cognitive Impairments

François Ledoyen·Gaël Dias·Alexis Lechervy·Jeremie Pantin·Fabrice Maurel·Youssef Chahir·Elisa Gouzonnat·Mélanie Berthelot·Stanislas Moravac·Armony Altinier·Amy Khairalla

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Abstract

Ensuring accessibility for individuals with cognitive impairments is essential for autonomy, self-determination, and full citizenship. However, manual Easy-to-Read (ETR) text adaptations are slow, costly, and difficult to scale, limiting access to crucial information in healthcare, education, and civic life. AI-driven ETR generation offers a scalable solution but faces key challenges, including dataset scarcity, domain adaptation, and balancing lightweight learning of Large Language Models (LLMs). In this paper, we introduce ETR-fr, the first dataset for ETR text generation fully compliant with European ETR guidelines. We implement parameter-efficient fine-tuning on PLMs and LLMs to establish generative baselines. To ensure high-quality and accessible outputs, we introduce an evaluation framework based on automatic metrics supplemented by human assessments. The latter is conducted using a 36-question evaluation form that is aligned with the guidelines. Overall results show that PLMs perform comparably to LLMs and adapt effectively to out-of-domain texts.

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François Ledoyen, Gaël Dias, Alexis Lechervy, Jeremie Pantin, Fabrice Maurel, Youssef Chahir, Elisa Gouzonnat, Mélanie Berthelot, Stanislas Moravac, Armony Altinier, Amy Khairalla. 2025-10-01. Inclusive Easy-to-Read Generation for Individuals with Cognitive Impairments. https://arxiv.org/abs/2510.00691

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