Hybridization of DBN with SVM and its Impact on Performance in Multi-Document Summarization

Abstract

Data available from web based sources has grown tremendously with growth of the internet. Users interested in information from such sources often use a search engine to obtain the data which they edit for presentation to their audience. This process can be tedious especially when it involves the generation of a summary. One way to ease the process is by automation of the summary generation process. Efforts by researchers towards automatic summarization have yielded several approaches among them machine learning. Thus, recommendations have been made on combining the algorithms with different strengths, also called hybridization, in order to enhance their performance. Therefore, this research sought to establish the impact of hybridization of Deep Belief Network (DBN) with Support Vector Machine (SVM) on precision, recall, accuracy and F-measure when used in the case of query oriented multi-document summarization. The experiments were carried out using data from National Institute of Standards and Technology (NIST), Document Understanding Conference (DUC) 2006. The data was split into training and test data and used appropriately in DBN, SVM, SVM-DBN hybrid and DBN-SVM hybrid. Results indicated that the hybridized algorithm has better precision, accuracy and F-measure as compared to DBN. Pre-classification hybridization of DBN with SVM (SVM-DBN) gives the best results. This research implies that use of DBN and SVM hybrid algorithms would enhance query oriented multi-document summarization.