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<Paper uid="P05-3013">
  <Title>Language Independent Extractive Summarization</Title>
  <Section position="6" start_page="51" end_page="51" type="concl">
    <SectionTitle>
4 Conclusion
</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 also take into account information recursively drawn from the entire text (graph).</Paragraph>
    <Paragraph position="1"> 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. In the process of identifying important sentences in a text, a sentence recommends other sentences that address 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="2"> An important aspect of the graph-based extractive summarization method is that it does not require deep linguistic knowledge, nor domain or language specific annotated corpora, which makes it highly portable to other domains, genres, or languages.</Paragraph>
  </Section>
class="xml-element"></Paper>