A Hybrid Approach for Extractive Document Summarization Using Machine Learning and Clustering Technique

Usually, presence of the same information in multiple documents is the main problem faced in effective information access. Instead of this redundant information thus accessed or retrieved, users are interested in retrieving information that addresses one or other several aspects. In such situation, text summarization proves to be very useful. Not only in Information retrieval, but it is an extremely active research topic in other fields like natural language processing and machine learning. Text summarization is a process of extracting content from a document and generating summary of that document thus presenting important content to user in a relatively condensed form. In this paper, study of several extractive text summarization approaches is made and an effective text summarization method is proposed. This method is based on Support-Vector-Machine (SVM). Proposed system tries to improve the performance and quality of the summary generated by the clustering technique by cascading it with SVM. Keywords— clustering, document summarization, extractive text summarization, machine learning, SVM.