  Complex text is a major barrier for many citizens when accessing public
information and knowledge. While often done manually, Text Simplification is a
key Natural Language Processing task that aims for reducing the linguistic
complexity of a text while preserving the original meaning. Recent advances in
Generative Artificial Intelligence (AI) have enabled automatic text
simplification both on the lexical and syntactical levels. However, as
applications often focus on English, little is understood about the
effectiveness of Generative AI techniques on low-resource languages such as
Dutch. For this reason, we carry out empirical studies to understand the
benefits and limitations of applying generative technologies for text
simplification and provide the following outcomes: 1) the design and
implementation for a configurable text simplification pipeline that
orchestrates state-of-the-art generative text simplification models, domain and
reader adaptation, and visualisation modules; 2) insights and lessons learned,
showing the strengths of automatic text simplification while exposing the
challenges in handling cultural and commonsense knowledge. These outcomes
represent a first step in the exploration of Dutch text simplification and shed
light on future endeavours both for research and practice.
