This study pioneers the use of synthetically generated data for training generative models in document-level text simplification of German texts. We demonstrate the effectiveness of our approach with real-world online texts. Addressing the challenge of data scarcity in language simplification, we crawled professionally simplified German texts and synthesized a corpus using GPT-4. We finetune Large Language Models with up to 13 billion parameters on this data and evaluate their performance. This paper employs various methodologies for evaluation and demonstrates the limitations of currently used rule-based metrics. Both automatic and manual evaluations reveal that our models can significantly simplify real-world online texts, indicating the potential of synthetic data in improving text simplification.

This paper presents NLP Lean Programming framework (NLPf), a new framework for creating custom Natural Language Processing (NLP) models and pipelines by utilizing common software development build systems. This approach allows developers to train and integrate domain-specific NLP pipelines into their applications seamlessly. Additionally, NLPf provides an annotation tool which improves the annotation process significantly by providing a well-designed GUI and sophisticated way of using input devices. Due to NLPf’s properties developers and domain experts are able to build domain-specific NLP application more effectively. Project page: https://gitlab.com/schrieveslaach/NLPf Video Tutorial: https://www.youtube.com/watch?v=44UJspVebTA (Demonstration starts at 11:40 min) This paper is related to: - Interfaces and resources to support linguistic annotation - Software architectures and reusable components - Software tools for evaluation or error analysis