Exploiting Summarization Data to Help Text Simplification

• URL: http://arxiv.org/abs/2302.07124v1
• Date: Tue, 14 Feb 2023 15:32:04 GMT
• Title: Exploiting Summarization Data to Help Text Simplification
• Authors: Renliang Sun, Zhixian Yang, Xiaojun Wan
• Abstract summary: We analyzed the similarity between text summarization and text simplification and exploited summarization data to help simplify. We named these pairs Sum4Simp (S4S) and conducted human evaluations to show that S4S is high-quality.
• Score: 50.0624778757462
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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