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<Paper uid="P05-3013">
  <Title>Language Independent Extractive Summarization</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Given the overwhelming amount of information available today, on the Web and elsewhere, techniques for efficient automatic text summarization are essential to improve the access to such information. Algorithms for extractive summarization are typically based on techniques for sentence extraction, and attempt to identify the set of sentences that are most important for the understanding of a given document.</Paragraph>
    <Paragraph position="1"> Some of the most successful approaches to extractive summarization consist of supervised algorithms that attempt to learn what makes a good summary by training on collections of summaries built for a relatively large number of training documents, e.g. (Hirao et al., 2002), (Teufel and Moens, 1997). However, the price paid for the high performance of such supervised algorithms is their inability to easily adapt to new languages or domains, as new training data are required for each new type of data. TextRank (Mihalcea and Tarau, 2004), (Mihalcea, 2004) is specifically designed to address this problem, by using an extractive summarization technique that does not require any training data or any language-specific knowledge sources. TextRank can be effectively applied to the summarization of documents in different languages without any modifications of the algorithm and without any requirements for additional data. Moreover, results from experiments performed on standard data sets have demonstrated that the performance of TextRank is competitive with that of some of the best summarization systems available today.</Paragraph>
  </Section>
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