Single-document and multi-document summarization techniques for email threads using sentence compression

Authors:
David M. Zajic;Bonnie J. Dorr;Jimmy Lin
Affiliations:
Department of Computer Science, University of Maryland, College Park, MD 20742, United States;Department of Computer Science, University of Maryland, College Park, MD 20742, United States;College of Information Studies, University of Maryland, College Park, MD 20742, United States
Venue:
Information Processing and Management: an International Journal
Year:
2008

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Abstract

We present two approaches to email thread summarization: collective message summarization (CMS) applies a multi-document summarization approach, while individual message summarization (IMS) treats the problem as a sequence of single-document summarization tasks. Both approaches are implemented in our general framework driven by sentence compression. Instead of a purely extractive approach, we employ linguistic and statistical methods to generate multiple compressions, and then select from those candidates to produce a final summary. We demonstrate these ideas on the Enron email collection - a very challenging corpus because of the highly technical language. Experimental results point to two findings: that CMS represents a better approach to email thread summarization, and that current sentence compression techniques do not improve summarization performance in this genre.