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<Paper uid="W04-3252">
  <Title>TextRank: Bringing Order into Texts</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Graph-based ranking algorithms like Kleinberg's HITS algorithm (Kleinberg, 1999) or Google's PageRank (Brin and Page, 1998) have been successfully used in citation analysis, social networks, and the analysis of the link-structure of the World Wide Web. Arguably, these algorithms can be singled out as key elements of the paradigm-shift triggered in the field of Web search technology, by providing a Web page ranking mechanism that relies on the collective knowledge of Web architects rather than individual content analysis of Web pages. 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.</Paragraph>
    <Paragraph position="1"> Applying a similar line of thinking to lexical or semantic graphs extracted from natural language documents, results in a graph-based ranking model that can be applied to 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="2"> In this paper, we introduce the TextRank graph-based ranking model for graphs extracted from natural language texts. We investigate and evaluate the application of TextRank to two language processing tasks consisting of unsupervised keyword and sentence extraction, and show that the results obtained with TextRank are competitive with state-of-the-art systems developed in these areas.</Paragraph>
  </Section>
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