In this paper, we apply transformer-based Natural Language Generation (NLG) techniques to the problem of text simplification. Currently, there are only a few German datasets available for text simplification, even fewer with larger and aligned documents, and not a single one with narrative texts. In this paper, we explore to which degree modern NLG techniques can be applied to German narrative text simplifications. We use Longformer attention and a pre-trained mBART model. Our findings indicate that the existing approaches for German are not able to solve the task properly. We conclude on a few directions for future research to address this problem.
@article{arxiv.2312.09907,
title = {Exploring Automatic Text Simplification of German Narrative Documents},
author = {Thorben Schomacker and Tillmann Dönicke and Marina Tropmann-Frick},
journal= {arXiv preprint arXiv:2312.09907},
year = {2023}
}