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What Makes a Good Summary?

  • Qunhua ZhaoEmail author
  • Eugene Santos
  • Hien Nguyen
  • Ahmed Mohamed
Chapter

Summary

One of the biggest challenges for intelligence analysts who participate in prevention or response to a terrorism act is to quickly find relevant information from massive amounts of data. Along with research on information retrieval and filtering, text summarization is an effective technique to help intelligence analysts shorten their time to find critical information and make timely decisions. Multi-document summarization is particularly useful as it serves to quickly describe a collection of information. The obvious shortcoming lies in what it cannot capture especially in more diverse collections. Thus, the question lies in the adequacy and/or usefulness of such summarizations to the target analyst. In this chapter, we report our experimental study on the sensitivity of users to the quality and content of multi-document summarization. We used the DUC 2002 collection for multi-document summarization as our testbed. Two groups of document sets were considered: (I) the sets consisting of closely correlated documents with highly overlapped content; and (II) the sets consisting of diverse documents covering a wide scope of topics. Intuitively, this suggests that creating a quality summary would be more difficult for the latter case. However, human evaluators were discovered to be fairly insensitive to this difference. This occurred when they were asked to rank the performance of various automated summarizers. In this chapter, we examine and analyze our experiments in order to better understand this phenomenon and how we might address it to improve summarization quality. In particular, we present a new metric based on document graphs that can distinguish between the two types of document sets.

Keywords

Human Judgment Document Graph Good Summary Ranking Approach African National Congress 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Qunhua Zhao
    • 1
    Email author
  • Eugene Santos
    • 1
  • Hien Nguyen
    • 2
  • Ahmed Mohamed
    • 3
  1. 1.Thayer School of Engineering Dartmouth College HanoverHanoverGermany
  2. 2.Department of Mathematical and Computer SciencesUniversity of WisconsinWhitewaterUSA
  3. 3.Department of Computer Science and EngineeringUniversity of Connecticut StorrsStorrsUSA

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