Exploiting Summarization Data to Help Text Simplification
This work addresses a data scarcity issue for researchers and practitioners in text simplification, though it is incremental as it builds on existing datasets and methods.
The paper tackled the problem of limited high-quality data for text simplification by exploiting summarization datasets, proposing an alignment algorithm and filtering method to create a new dataset called Sum4Simp (S4S), which improved the performance of mainstream simplification models, especially in low-resource scenarios.
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.