Text Summarization Techniques: A Brief Survey <s> Email Summarization <s> This paper describes an approach to digesting threads of archived discussion lists by clustering messages into approximate topical groups, and then extracting shorter overviews, and longer summaries for each group. <s> BIB001 </s> Text Summarization Techniques: A Brief Survey <s> Email Summarization <s> Summarizing threads of email is different from summarizing other types of written communication as it has an inherent dialog structure. We present initial research which shows that sentence extraction techniques can work for email threads as well, but profit from email-specific features. In addition, the presentation of the summary should take into account the dialogic structure of email communication. <s> BIB002
Email has some distinct characteristics that indicates the aspects of both spoken conversation and written text. For example, summarization techniques must consider the interactive nature of the dialog as in spoken conversations. Nenkova et al.  presented early research in this regard, by proposing a method to generate a summary for the first two levels of the thread discussion. A thread consists of one or more conversations between two or more participants over time. They select a message from the root message and from each response to the root, considering the overlap with root context. Rambow et al. BIB002 used a machine learning technique and included features related to the thread as well as features of the email structure such as position of the sentence in the tread, number of recipients, etc. Newman et al. BIB001 describe a system to summarize a full mailbox rather than a single thread by clustering messages into topical groups and then extracting summaries for each cluster.