Event Detection and Summarization Based on Social Networks and Semantic Query Expansion

Abstract

Events can be characterized by a set of descriptive, collocated keywords extracted documents. Intuitively, documents describing the same event will contain similar sets of keywords, and the graph for a document collection will contain clusters individual events. Helping users to understand the event is an acute problem nowadays as the users are struggling to keep up with tremendous amount of information published every day in the Internet. The challenging task is to detect the events from online web resources, it is getting more attentions. The important data source for event detection is a Web search log because the information it contains reflects users' activities and interestingness to various real world events. There are three major issues playing role for event detection from web search logs: effectiveness, efficiency of detected events. We focus on modeling the content of events by their semantic relations with other events and generating structured summarization. Event mining is a useful way to understand computer system behaviors. The focus of recent works on event mining has been shifted to event summarization from discovering frequent patterns. Event summarization provides a comprehensible explanation of the event sequence based on certain aspects.