# Data and Approaches for German Text simplification -- towards an Accessibility-enhanced Communication

arXiv: https://arxiv.org/abs/2312.09966 · HTML: https://mlsanity.com/paper/2312.09966/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 33 references that are in the map.
- First posted on arXiv 15 Dec 2023.

## Builds on (most cited first, 6 of 6)

- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [Plain English Summarization of Contracts](https://mlsanity.com/paper/1906.00424/) (2019)
- [A Corpus for Automatic Readability Assessment and Text Simplification of German](https://mlsanity.com/paper/1909.09067/) (2019)
- [Language Models for German Text Simplification: Overcoming Parallel Data Scarcity through Style-specific Pre-training](https://mlsanity.com/paper/2305.12908/) (2023)
- [DEPLAIN: A German Parallel Corpus with Intralingual Translations into Plain Language for Sentence and Document Simplification](https://mlsanity.com/paper/2305.18939/) (2023)
- [A New Aligned Simple German Corpus](https://mlsanity.com/paper/2209.01106/) (2022)

## Built on by (most cited first, 2 of 2)

- [Images Speak Volumes: User-Centric Assessment of Image Generation for Accessible Communication](https://mlsanity.com/paper/2410.03430/) (2024)
- [German4All -- A Dataset and Model for Readability-Controlled Paraphrasing in German](https://mlsanity.com/paper/2508.17973/) (2025)

## Abstract (arXiv)

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