A Graph Based Query Focused Multi-Document Summarization

A user's information need, normally represented as a search query, can be satisfied 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 modified spreading activation theory, where the query graph is also integrated during the spreading activation over the global graph. The proposed system shows significant improvements in generating summaries when compared to other existing summarization systems.