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.
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Wang, B., Liu, B., Sun, C., Wang, X., Li, B. (2009). Adaptive Maximum Marginal Relevance Based Multi-email Summarization. In: Deng, H., Wang, L., Wang, F.L., Lei, J. (eds) Artificial Intelligence and Computational Intelligence. AICI 2009. Lecture Notes in Computer Science(), vol 5855. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05253-8_46
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DOI: https://doi.org/10.1007/978-3-642-05253-8_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-05252-1
Online ISBN: 978-3-642-05253-8
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