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<Paper uid="P04-3020">
  <Title>Graph-based Ranking Algorithms for Sentence Extraction, Applied to Text Summarization</Title>
  <Section position="8" start_page="54" end_page="54" type="concl">
    <SectionTitle>
6 Conclusions
</SectionTitle>
    <Paragraph position="0"> Intuitively, TextRank works well because it does not only rely on the local context of a text unit (vertex), but rather it takes into account information recursively drawn from the entire text (graph). Through the graphs it builds on texts, TextRank 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"> An important aspect of TextRank 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. null</Paragraph>
  </Section>
class="xml-element"></Paper>