AUTOMATIC TEXT SUMMARIZATION USING SUPPORT VECTOR MACHINE

This work investigates different text features to select the best one and proposes an approach to address automatic text summarization. This approach is a trainable summarizer, which takes into account several features, including sentence position, sentence centrality, sentence resemblance to the title, sentence inclusion of name entity, sentence inclusion of numerical data, sentence relative length, Bushy path of the sentence and aggregated similarity for each sentence to generate summaries. First we investigate the effect of each sentence feature on the summarization task. Then we use all features score function to train Support Vector Machine (SVM) in order to construct a text summarizer model. The proposed approach performance is measured at several compression rates (CR) on a data corpus composed of 100 English articles from the domain of politics.