Indonesian Text Summarization based on Naïve Bayes Method

Abstract

In this paper we present a system for generating summary by sentence extraction. To determine the weight of sentence, we use text features, such as sentence position, sentence relative length, average term frequency, keyword extraction, key phrase extraction, sentence similarity to the title, sentence centrality, inclusion of numerical data, inclusion of entity name, and inclusion of news emphasize words. We also investigate the effect of semantic feature, using latent semantic analysis, on the summarization task. Our experiments show that semantic feature increases precision and F-measure by 9.8% and 2.4% respectively in case of 20% Compression Rate.
Key words: process, reduce, computer, method