Adaptive Maximum Marginal Relevance Based Multi-email Summarization

Authors:
Baoxun Wang;Bingquan Liu;Chengjie Sun;Xiaolong Wang;Bo Li
Affiliations:
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China 150001;School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China 150001;School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China 150001;School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China 150001;School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China 150001
Venue:
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
Year:
2009

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Abstract

By analyzing the inherent relationship between the maximum marginal relevance (MMR) model and the content cohesion of emails with the same subject, this paper presents an adaptive maximum marginal relevance based multi-email summarization method. Due to the adoption of approximate computing of email content cohesion, the adaptive MMR is able to automatically adjust the parameters according to the changing of the email sets. The experimental results have shown that the email summarizing system based on this technique can increase the precision while reducing the redundancy of the automatic summary results, consequently improve the average quality of email summaries.