Exploiting Summarization Data to Help Text Simplification

TL;DR

This paper leverages summarization datasets to enhance text simplification by developing an alignment method, filtering high-quality pairs, and demonstrating improved model performance, especially in low-resource settings.

Contribution

It introduces a novel alignment algorithm and filtering method to extract high-quality simplification pairs from summarization data, expanding resources for text simplification.

Findings

S4S pairs are high-quality and comparable to real simplification datasets.

Using S4S improves simplification model performance in low-resource scenarios.

The approach broadens data sources for text simplification research.

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.