Paper: Summarizing Emails with Conversational Cohesion and Subjectivity

ACL ID P08-1041
Title Summarizing Emails with Conversational Cohesion and Subjectivity
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2008
Authors

In this paper, we study the problem of sum- marizing 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 Page- Rank, 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 co- hesion measures. Moreover, subjective words can significantly improve accuracy.