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

Exploiting Summarization Data to Help Text Simplification