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

• URL: http://arxiv.org/abs/2510.00691v1
• Date: Wed, 01 Oct 2025 09:13:18 GMT
• Title: Inclusive Easy-to-Read Generation for Individuals with Cognitive Impairments
• Authors: François Ledoyen, Gaël Dias, Alexis Lechervy, Jeremie Pantin, Fabrice Maurel, Youssef Chahir, Elisa Gouzonnat, Mélanie Berthelot, Stanislas Moravac, Armony Altinier, Amy Khairalla,
• Abstract summary: We introduce ETR-fr, the first dataset for ETR text generation fully compliant with European ETR guidelines.<n>We implement parameter-efficient fine-tuning on PLMs and LLMs to establish generative baselines.<n>Overall results show that PLMs perform comparably to LLMs and adapt effectively to out-of-domain texts.
• Score: 2.1481398044731574
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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