16 International Journal of Intelligent Information Technologies, 10(1), 16-41, January-March 2014 Copyright © 2014, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. ABSTRACT A user’s information need, normally represented as a search query, can be satisfed by creating a query focused coherent and readable summary, by fusing the relevant parts of information from multiple documents. While aggregating the information from multiple documents, the quality of the summary is improved by eliminating redundant information from the document set. In this paper, we focus on removing such redundant information and identifying the essential components from multiple documents (represented as a single global semantic graph), with respect to the given query (represented as a query graph). While the redundancy elimination is carried out using various levels of graph matching which are then indicated through canonical labeling of graphs, the selection of essential components for a query focused summary is performed, through the modi- fed spreading activation theory, where the query graph is also integrated during the spreading activation over the global graph. The proposed system shows signifcant improvements in generating summaries when compared to other existing summarization systems. A Graph Based Query Focused Multi-Document Summarization J Balaji, Department of Computer Science and Engineering, Anna University, Chennai, India T V Geetha, Department of Computer Science and Engineering, Anna University, Chennai, India Ranjani Parthasarathi, Department of Information Science and Technology, Anna University, Chennai, India Keywords: Multi-Document Summarization, Query Focused Summary, Redundancy Elimination, Semantic Graphs, Spreading Activation, Universal Networking Language INTRODUCTION With a wide variety of documents available in the web, text summarization is one of the im- portant tasks, which effectively compresses the information in a document(s). Multi-document summarization is a task of identifying the important common themes and/or aspects of multiple documents. The primary tasks in multi-document summarization are the identification of simi- larities and differences between documents (Wan & Yang, 2008). One of the challenges of multi-document summarization is that a set of documents might contain diverse informa- tion, which is either related or unrelated to the particular topic. Therefore, effective methods are needed to analyze the information stored in different documents, and abstract the glob- ally important information to reflect the main topic. In single-document summarization, the sentences in a document are unique and may not have redundant information. DOI: 10.4018/ijiit.2014010102