Topic-driven multi-document summarization with encyclopedic knowledge and spreading activation

Abstract

Information of interest to users is often distributed over a set of documents. Users can specify their request for information as a query/topic - a set of one or more sentences or questions. Producing a good summary of the relevant information relies on understanding the query and linking it with the associated set of documents. To "understand" the query we expand it using encyclopedic knowledge in Wikipedia. The expanded query is linked with its associated documents through spreading activation in a graph that represents words and their grammatical connections in these documents. The topic expanded words and activated nodes in the graph are used to produce an extractive summary. The method proposed is tested on the DUC summarization data. The system implemented ranks high compared to the participating systems in the DUC competitions, confirming our hypothesis that encyclopedic knowledge is a useful addition to a summarization system. © 2008 Association for Computational Linguistics.