Relevance Vector Machine for Summarization

Abstract

This research aimed at finding relevances Vector Machine for summarization. The needed of producing an automatics text summarization create the research of text summarization continues to develop. One way to create an automatic summarization is by choosing the sentences which contain the main topics and reassembled them into a summary. The usage of Supports Vector Machine method (SVM) able to select summary sentences. The Relevance Vector Machine (RVM) appears as a further development of the SVM. This method performs a good result in a classification of Magnetic Resonance Imaging (MRI) data. Therefore, in this research, it examined the ability of RVM in the text summarization. Extracting the sentences used eight features, they are the length of the sentence, the sentence position, the containing of numerical data, the thematic words in the sentence, the similarity of the title, the sentence similarity, the sentence lexical cohesion before and after. There are 1509 training sentences and 214 testing sentences from 100 text documents. The result showed that using Radial Basis Function the accuracy of the RVM reached 63.084%. The RVM performance shows a better result than the SVM, 2% higher than the SVM result and uses fewer vector supports.