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<Paper uid="W04-3247">
  <Title>LexPageRank: Prestige in Multi-Document Text Summarization</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Text summarization is the process of automatically creating a compressed version of a given text that provides useful information for the user. In this paper, we focus on multi-document generic text summarization, where the goal is to produce a summary of multiple documents about the same, but unspecified topic.</Paragraph>
    <Paragraph position="1"> Our summarization approach is to assess the centrality of each sentence in a cluster and include the most important ones in the summary. In Section 2, we present centroid-based summarization, a well-known method for judging sentence centrality. Then we introduce two new measures for centrality, Degree and LexPageRank, inspired from the &amp;quot;prestige&amp;quot; concept in social networks and based on our new approach. We compare our new methods and centroid-based summarization using a feature-based generic summarization toolkit, MEAD, and show that new features outperform Centroid in most of the cases.</Paragraph>
    <Paragraph position="2"> Test data for our experiments is taken from Document Understanding Conferences (DUC) 2004 summarization evaluation to compare our system also with other state-of-the-art summarization systems.</Paragraph>
  </Section>
class="xml-element"></Paper>