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. Code and datasets are available at https://github.com/FrLdy/ETR-fr.

Simplifying complex texts is essential to ensure equitable access to information, particularly for individuals with cognitive impairments.The Easy-to-Read (ETR) initiative provides a framework to make content more accessible for these individuals.However, manually creating such texts remains time-consuming and resourceintensive.In this work, we investigate the potential of large language models (LLMs) to automate the generation of ETR content.To address the scarcity of aligned corpora and the specific constraints of ETR, we propose a multitask learning (MTL) approach that trains models jointly on text summarization, text simplification, and ETR generation.We explore two complementary strategies: multi-task retrievalaugmented generation (RAG) for in-context learning (ICL), and MTL-LoRA for parameterefficient fine-tuning (PEFT).Our experiments with Mistral-7B and LLaMA-3-8B, conducted on ETR-fr, a new high-quality dataset, show that MTL-LoRA consistently outperforms all other strategies in in-domain settings, while the MTL-RAG-based approach achieves better generalization in out-of-domain scenarios.