Intelligent email: aiding users with AI

Abstract

Email occupies a central role in the modern workplace. This has led to a vast increase in the number of email messages that users are expected to handle daily. Further-more, email is no longer simply a tool for asynchronous online communication—email is now used for task man-agement, personal archiving, as well both synchronous and asynchronous online communication (Whittaker and Sidner 1996). This explosion can lead to “email overload”—many users are overwhelmed by the large quantity of informa-tion in their mailboxes. In the human–computer interaction community, there has been much research on tackling email overload. Recently, similar efforts have emerged in the ar-tificial intelligence (AI) and machine learning communities to form an area of research known as intelligent email. In this paper, we take a user-oriented approach to applying AI to email. We identify enhancements to email user in-terfaces and employ machine learning techniques to support these changes. We focus on three tasks—summary keyword generation, reply prediction and attachment prediction—and summarize recent work in these areas. Keyword Summarization Email inboxes typically display a limited amount of infor-mation about each email, usually the subject, sender and date. Users are then expected to perform email triage— the process of making decisions about how to handle these emails—based on this information. In practice, such limited information is often insufficient to perform good triage and can lead to missed messages or wasted time. Additional con-cise and relevant information about each message can speed up the decision-making process and reduce errors. Muresan, Tzoukermann, and Klavans (2001) introduced the

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