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<Paper uid="P04-3020">
  <Title>Graph-based Ranking Algorithms for Sentence Extraction, Applied to Text Summarization</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Graph-based ranking algorithms, such as Kleinberg's HITS algorithm (Kleinberg, 1999) or Google's PageRank (Brin and Page, 1998), have been traditionally and successfully used in citation analysis, social networks, and the analysis of the link-structure of the World Wide Web. In short, a graph-based ranking algorithm is a way of deciding on the importance of a vertex within a graph, by taking into account global information recursively computed from the entire graph, rather than relying only on local vertex-specific information. null A similar line of thinking can be applied to lexical or semantic graphs extracted from natural language documents, resulting in a graph-based ranking model called TextRank (Mihalcea and Tarau, 2004), which can be used for a variety of natural language processing applications where knowledge drawn from an entire text is used in making local ranking/selection decisions. Such text-oriented ranking methods can be applied to tasks ranging from automated extraction of keyphrases, to extractive summarization and word sense disambiguation (Mihalcea et al., 2004).</Paragraph>
    <Paragraph position="1"> In this paper, we investigate a range of graph-based ranking algorithms, and evaluate their application to automatic unsupervised sentence extraction in the context of a text summarization task. We show that the results obtained with this new unsupervised method are competitive with previously developed state-of-the-art systems.</Paragraph>
  </Section>
class="xml-element"></Paper>