Assessing Sentence Simplification Methods Applied to Text Summarization

Automatic text summarization is proving itself useful to sieve relevant content from the Internet and digital libraries with reduced human effort. Nevertheless, extractive summarization approaches have limitations, possibly not fully capturing the informativeness of a text. A potential strategy to address this problem is the adoption of sentence simplification methods. This work focuses on the evaluation of sentence simplification methods as a preprocessing step for extractive text summarization in order to answer the question of whether sentence simplification increases the informativeness of extractive summaries. Four different sentence simplification methods, two being simple filters and the other two performing rule-based transformations, are assessed here in order to point out the best method for such a purpose. Fifteen sentence scoring methods for summarization are applied in combination with the simplification methods to a corpus of 1,038 news articles in English. The results suggest that the transformation approaches, which take into account linguistic features and grammaticality, achieve the best performance.