Summarizing Emails with Conversational Cohesion and Subjectivity

In this paper, we study the problem of summarizing email conversations. We first build a sentence quotation graph that captures the conversation structure among emails. We adopt three cohesion measures: clue words, semantic similarity and cosine similarity as the weight of the edges. Second, we use two graph-based summarization approaches, Generalized ClueWordSummarizer and PageRank, to extract sentences as summaries. Third, we propose a summarization approach based on subjective opinions and integrate it with the graph-based ones. The empirical evaluation shows that the basic clue words have the highest accuracy among the three cohesion measures. Moreover, subjective words can significantly improve accuracy.

• Type: Conference
• Year: 2008
• Where: ACL
• Authors: Giuseppe Carenini, Raymond T. Ng, Xiaodong Zhou
• Keywords: ACL 2008, Clue Words, Cohesion Measures, Computational Linguistics, Graph-based Summarization