One of the major problems with text simplification is the lack of high-quality data. The sources of simplification datasets are limited to Wikipedia and Newsela, restricting further development of this field. In this paper, we analyzed the similarity between text summarization and text simplification and exploited summarization data to help simplify. First, we proposed an alignment algorithm to extract sentence pairs from summarization datasets. Then, we designed four attributes to characterize the degree of simplification and proposed a method to filter suitable pairs. We named these pairs Sum4Simp (S4S). Next, we conducted human evaluations to show that S4S is high-quality and compared it with a real simplification dataset. Finally, we conducted experiments to illustrate that the S4S can improve the performance of several mainstream simplification models, especially in low-resource scenarios.

翻译：文本简化的主要问题之一是缺少高质量的数据。简化数据集的来源仅限于维基百科和Newsela,限制了这一领域的进一步发展。在本文中,我们分析了文本概要和文本简化之间的相似性,并利用了汇总数据来帮助简化。首先,我们建议了一种调整算法,从汇总数据集中提取对句。然后,我们设计了四个属性来说明简化程度的特点,并提出了筛选合适配对的方法。我们给这两对定了名字:Sum4Simp(S4S)。接下来,我们进行了人类评估,以表明S4S是高质量的,并将其与真正的简化数据集进行比较。最后,我们进行了实验,以说明S4S可以改进几个主流简化模型的性能,特别是在资源匮乏的情况下。