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<Paper uid="C04-1077">
  <Title>Corpus and Evaluation Measures for Multiple Document Summarization with Multiple Sources</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> It has been said that we have too much information on our hands, forcing us to read through a great number of documents and extract relevant information from them. With a view to coping with this situation, research on automatic text summarization has attracted a lot of attention recently and there have been many studies in this field. There is a particular need to establish methods for the automatic summarization of multiple documents rather than single documents.</Paragraph>
    <Paragraph position="1"> There have been several evaluation workshops on text summarization. In 1998, TIPSTER SUM-MAC (Mani et al., 2002) took place and the Document Understanding Conference (DUC)1 has been held annually since 2001. DUC has included multiple document summarization among its tasks since the first conference. The Text Summarization Challenge (TSC)2 has been held once in one and a half years as part of the NTCIR (NII-NACSIS Test Collection for IR Systems) project since 2001. Multiple document summarization was included for the first time as one of the tasks at TSC2 (in 2002) (Okumura et al., 2003). Multiple document summarization is now a central issue for text summarization research.</Paragraph>
    <Paragraph position="2">  In this paper, we detail the corpus construction and evaluation measures used at the Text Summarization Challenge 3 (TSC3 hereafter), where multiple document summarization is the main issue. We also report the results of a preliminary experiment on simple multiple document summarization systems. null</Paragraph>
  </Section>
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