The production of accurate and complete multiple-document summaries is challenged by the complexity of judging the usefulness of information to the user. Our aim is to determine whether identifying sub-events in a news topic could help us capture essential information to produce better summaries. In our first experiment, we asked human judges to determine the relative utility of sentences as they related to the subevents of a larger topic. We used this data to create summaries by three different methods, and we then compared these summaries with three automatically created summaries. In our second experiment, we show how the results of our first experiment can be applied to a cluster-based automatic summarization system. Through both experiments, we examine the use of inter-judge agreement and a relative utility metric that accounts for the complexity of determining sentence quality in relation to a topic.
REF described identification of sub-events in multiple documents.
Sub-Event Based Multi-Document Summarization
Workshop On Text Summarization
Computer Science