An Assessment of Sentence Simplification Methods in Extractive Text Summarization
Rafaella F. Vale, Rafael Dueire Lins, Rafael Fernandes de Abreu e Lima Ferreira · 2020
The unprecedented growth of textual content on the Web made essential the development of automatic or semi-automatic techniques to help people to find valuable information in such a huge heap of text data. Automatic text summarization is one of such techniques that is being pointed out as offering a viable solution in such a chaotic scenario. Extractive text summarization, in particular, selects a set of sentences from a text according to specific criteria. Strategies for extractive summarization can benefit from preprocessing techniques that emphasize the relevance or infor-mativeness of sentences with respect to the selection criteria. This paper tests such a hypothesis using sentence simplification methods. Four methods are used to simplify a corpus of news articles in English: a rule-based method, an optimization method, a supervised deep learning model and an unsupervised deep learning model. The simplified outputs are summarized using 14 sentence selection strategies. The combinations of simplification and summarization methods are compared with the baseline --- the summarized corpus without previous simplification --- with a quantitative analysis, which suggests sentence compression with restrictions and models learned from large parallel corpora tend to perform better and yield gains over summarization without prior simplification.