Extractive summarization based on event term clustering

Event-based summarization extracts and organizes summary sentences in terms of the events that the sentences describe. In this work, we focus on semantic relations among event terms. By connecting terms with relations, we build up event term graph, upon which relevant terms are grouped into clusters. We assume that each cluster represents a topic of documents. Then two summarization strategies are investigated, i.e. selecting one term as the representative of each topic so as to cover all the topics, or selecting all terms in one most significant topic so as to highlight the relevant information related to this topic. The selected terms are then responsible to pick out the most appropriate sentences describing them. The evaluation of clustering-based summarization on DUC 2001 document sets shows encouraging improvement over the well-known PageRank-based summarization.