Real-Time Web Scale Event Summarization Using Sequential Decision Making
We present a system based on sequential decision making for the online summarization of massive document streams, such as those found on the web. Given an event of interest (e.g. "Boston marathon bombing"), our system is able to filter the stream for relevance and produce a series of short text updates describing the event as it unfolds over time. Unlike previous work, our approach is able to jointly model the relevance, comprehensiveness, novelty, and timeliness required by time-sensitive queries. We demonstrate a 28.3% improvement in summary F1 and a 43.8% improvement in time-sensitive F1 metrics.
Chris Kedzie is a 3rd year Ph.D. candidate in the Department of Computer Science at Columbia University, working in the field of Natural Language Processing under Prof. Kathleen McKeown. His research focuses on predictive models for summarization of news streams. He is also interested in language generation for summarization. Before studying computer science, he played classical guitar and studied music theory/composition at Loyola Marymount University.