Combining Multiple Features for Automatic Text Summarization through Machine Learning

In this paper we explore multiple features for extractive automatic summarization using machine learning. They account for SuPor-2 features, a supervised summarizer for Brazilian Portuguese, and graph-based features mirroring complex networks measures. Four different classifiers and automatic feature selection are explored. ROUGE is used for assessment of single-document summarization of news texts.