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Refereed Papers

Track: Data Mining

Paper Title:
Summarizing Email Conversations with Clue Words


With the ever increasing popularity of emails, email overload becomes a major problem for email users. Email summarization is one way not only to solve this problem, but also to make use of one's email corpus. In this paper, we propose a new framework for email summarization. One novelty is to use a fragment quotation graph to try to capture an email conversation. The second novelty is to use clue words to measure the importance of sentences in conversation summarization. Based on clue words and their scores, we propose a method called CWS, which is capable of producing a summary of any length as requested by the user. We provide a comprehensive comparison of CWS with various existing methods on the Enron data set. Preliminary results suggest that CWS provides better summaries than existing methods.

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