Year: 2003
Type: conference-paper
Abstract: 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 sub-events 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.
Authors: Naomi Daniel, Dragomir Radev, Timothy Allison
Institution: University of Michigan
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W1999315683