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.

应用基于转换器的自然语言生成技术来进行文本简化研究，发现现有的德语方法无法很好地解决该问题，并提出了一些未来研究的方向。

德国叙事文档的自动文本简化探索

Exploring Automatic Text Simplification of German Narrative Documents

This paper presents a comparison of unsupervised methods of hypernymy
prediction (i.e., to predict which word in a pair of words such as fish-cod is
the hypernym and which the hyponym). Most importantly, we demonstrate across
datasets for English and for German that the predictions of three methods
(WeedsPrec, invCL, SLQS Row) strongly overlap and are highly correlated with
frequency-based predictions. In contrast, the second-order method SLQS shows an
overall lower accuracy but makes correct predictions where the others go wrong.
Our study once more confirms the general need to check the frequency bias of a
computational method in order to identify frequency-(un)related effects.

该论文比较了无监督的上位词预测方法，特别是在英语和德语的数据集上证明了三种方法的预测结果与基于频率的预测结果高度重叠和高度相关；然而，第二种方法 SLQS 虽然整体准确度较低，但在其他方法出错时可以进行正确的预测。该研究再次证实了需要检查计算方法中的频率偏见以识别与频率相关的效应的普遍需求。