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<Paper uid="I05-2004">
  <Title>A Language Independent Algorithm for Single and Multiple Document Summarization</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> 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 overall understanding of a given document. Some of the most successful approaches 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 data type. In this paper, we show that a method for extractive summarization relying on iterative graph-based algorithms, as previously proposed in (Mihalcea and Tarau, 2004) can be applied to the summarization of documents in different languages without any requirements for additional data. Additionally, we also show that a layered application of this single-document summarization method can result into an efficient multi-document summarization tool.</Paragraph>
    <Paragraph position="1"> Earlier experiments with graph-based ranking algorithms for text summarization, as previously reported in (Mihalcea and Tarau, 2004) and (Erkan and Radev, 2004), were either limited to single-document English summarization, or they were applied to English multi-document summarization, but in conjunction with other extractive summarization techniques that did not allow for a clear evaluation of the impact of the graph algorithms alone. In this paper, we show that a method exclusively based on graph-based algorithms can be successfully applied to the summarization of single and multiple documents in any language, and show that the results are competitive with those of state-of-the-art summarization systems.</Paragraph>
    <Paragraph position="2"> The paper is organized as follows. Section 2 briefly overviews two iterative graph-based ranking algorithms, and shows how these algorithms can be applied to single and multiple document summarization. Section 3 describes the data sets used in the summarization experiments and the evaluation methodology. Experimental results are presented in Section 4, followed by discussions, pointers to related work, and conclusions.</Paragraph>
  </Section>
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