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.
@article{arxiv.2510.00691,
title = {Inclusive Easy-to-Read Generation for Individuals with Cognitive Impairments},
author = {François Ledoyen and Gaël Dias and Alexis Lechervy and Jeremie Pantin and Fabrice Maurel and Youssef Chahir and Elisa Gouzonnat and Mélanie Berthelot and Stanislas Moravac and Armony Altinier and Amy Khairalla},
journal= {arXiv preprint arXiv:2510.00691},
year = {2025}
}