ARTIST: ARTificial Intelligence for Simplified Text

TL;DR

This paper introduces ARTIST, a configurable pipeline for automatic text simplification using generative AI, focusing on Dutch language, and discusses its benefits, limitations, and future research directions.

Contribution

It presents a novel configurable pipeline for Dutch text simplification leveraging state-of-the-art generative models and provides empirical insights into their effectiveness and challenges.

Findings

Strengths of automatic text simplification are demonstrated.

Challenges include handling cultural and commonsense knowledge.

First exploration of Dutch text simplification using generative AI.

Abstract

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.