Event-Based Summarization Using Time Features

Authors:
Mingli Wu;Wenjie Li;Qin Lu;Kam-Fai Wong
Affiliations:
Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong;Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong;Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong;Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong
Venue:
CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
Year:
2009

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Abstract

We investigate whether time features help to improve event-based summarization. In this paper, events are defined as event terms and the associated event elements. While event terms represent the actions themselves, event elements denote action arguments. After anchoring events on the time line, two different statistical measures are employed to identify importance of events on each day. Experiments show that the combination of tf*idfweighting scheme and time features can improve the quality of summaries significantly. The improvement can be attributed to its capability to represent the trend of news topics depending on event temporal distributions.