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 cohesion measures: clue words, semantic similarity 
and cosine similarity as the weight of the edges. Then, we use two graph-based 
summarization approaches, Generalized ClueWordSummarizer and Page- Rank. 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 improve accuracy significantly.