This paper examines the current state-of-the-art of German text simplification, focusing on parallel and monolingual German corpora. It reviews neural language models for simplifying German texts and assesses their suitability for legal texts and accessibility requirements. Our findings highlight the need for additional training data and more appropriate approaches that consider the specific linguistic characteristics of German, as well as the importance of the needs and preferences of target groups with cognitive or language impairments. The authors launched the interdisciplinary OPEN-LS project in April 2023 to address these research gaps. The project aims to develop a framework for text formats tailored to individuals with low literacy levels, integrate legal texts, and enhance comprehensibility for those with linguistic or cognitive impairments. It will also explore cost-effective ways to enhance the data with audience-specific illustrations using image-generating AI. For more and up-to-date information, please visit our project homepage https://open-ls.entavis.com

翻译：本文探讨了德语文本简化的最新技术现状，重点分析了平行语料和单语德语语料库。研究回顾了用于简化德语文本的神经语言模型，并评估了其在法律文本和可访问性要求中的适用性。我们的研究结果强调了需要更多训练数据和更合适的方法，这些方法需考虑德语的特定语言特征，以及认知或语言障碍目标群体的需求和偏好的重要性。作者于2023年4月启动了跨学科的OPEN-LS项目，以解决这些研究空白。该项目旨在开发一种针对低识字水平人群定制的文本格式框架，整合法律文本，并提高对语言或认知障碍人群的可理解性。项目还将探索利用图像生成AI以经济高效的方式为数据添加受众特定插图的方法。欲获取更新信息，请访问项目主页：https://open-ls.entavis.com