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<Paper uid="I05-2004">
  <Title>A Language Independent Algorithm for Single and Multiple Document Summarization</Title>
  <Section position="7" start_page="23" end_page="23" type="concl">
    <SectionTitle>
5 Conclusions
</SectionTitle>
    <Paragraph position="0"> Intuitively, iterative graph-based ranking algorithms work well on the task of extractive summarization because they do not only rely on the local context of a text unit (vertex), but they rather take into account information recursively drawn from the entire text (graph). Through the graphs it builds on texts, a graph-based ranking algorithm identifies connections between various entities in a text, and implements the concept of recommendation. A text unit recommends other related text units, and the strength of the recommendation is recursively computed based on the importance of the units making the recommendation. In the process of identifying important sentences in a text, a sentence recommends another sentence that addresses similar concepts as being useful for the overall understanding of the text. Sentences that are highly recommended by other sentences are likely to be more informative for the given text, and will be therefore given a higher score.</Paragraph>
    <Paragraph position="1"> In this paper, we showed that a previously proposed method for graph-based extractive summarization can be successfully applied to the summarization of documents in different languages, without any requirements for additional knowledge or corpora. Moreover, we showed how a meta-summarizer relying on a layered application of techniques for single-document summarization can be turned into an effective method for multi-document summarization. Experiments performed on standard data sets have shown that the results obtained with this method are comparable with those of state-of-the-art systems for automatic summarization, while at the same time providing the benefits of a robust language independent algorithm.</Paragraph>
  </Section>
class="xml-element"></Paper>