Ensemble of Support Vector Machine and Ontological Structures to Generate Abstractive Text Summarization

Abstract

Automatic summarization systems are much needed to lessen the information overload which is being faced by people due to exponential growth of data on World Wide Web. These systems choose the most significant part of the text from a single document or multiple documents and present the compressed surrogate form of the complete information which was intended to be conveyed. In this research paper, we propose an approach to generate summary from a given text first by extracting the most relevant sentences and then making further concise by creating ontological structures of these sentences and then generating the abstractive summary from these structures. Our proposed system is evaluated with DUC 2002 data set and it is found that the performance of this system as evaluated using ROUGE-1 is 58.175 which is better than other state of the art systems. The values reported in the experimental process of the research report the significant contribution of this innovative method.