Exploring Automatic Text Simplification of German Narrative Documents
This addresses the lack of resources and methods for simplifying German narrative texts, which is an incremental contribution to the field of NLP.
The paper tackled the problem of automatic text simplification for German narrative documents using transformer-based NLG techniques, finding that existing approaches were insufficient for the task and concluding with directions for future research.
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.