Combining Multiple Features for Automatic Text Summarization through Machine Learning

Authors:
Daniel Saraiva Leite;Lucia Helena Rino
Affiliations:
Departamento de Computação, UFSCar, Núcleo Interinstitucional de Lingüística Computacional, São Carlos, Brazil 13565-905;Departamento de Computação, UFSCar, Núcleo Interinstitucional de Lingüística Computacional, São Carlos, Brazil 13565-905
Venue:
PROPOR '08 Proceedings of the 8th international conference on Computational Processing of the Portuguese Language
Year:
2008

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Abstract

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.