Exploring Automatic Text Simplification of German Narrative Documents
Thorben Schomacker Tillmann Dönicke Marina Tropmann-Frick
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.