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.
