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<Paper uid="P03-2021">
  <Title>iNeATS: Interactive Multi-Document Summarization</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
2 NeATS
</SectionTitle>
    <Paragraph position="0"> NeATS (Lin and Hovy, 2002) is an extraction-based multi-document summarization system. It is among the top two performers in DUC 2001 and 2002 (Over, 2001). It consists of three main components: null Content Selection The goal of content selection is to identify important concepts mentioned in a document collection. NeATS computes the likelihood ratio (Dunning, 1993) to identify key concepts in unigrams, bigrams, and trigrams and clusters these concepts in order to identify major subtopics within the main topic. Each sentence in the document set is then ranked, using the key concept structures. These n-gram key concepts are called topic signatures.</Paragraph>
    <Paragraph position="1"> Content Filtering NeATS uses three different filters: sentence position, stigma words, and redundancy filter. Sentence position has been used as a good important content filter since the late 60s (Edmundson, 1969). NeATS applies a simple sentence filter that only retains the N lead sentences. Some sentences start with conjunctions, quotation marks, pronouns, and the verb &amp;quot;say&amp;quot; and its derivatives. These stigma words usually cause discontinuities in summaries. The system reduces the scores of these sentences to demote their ranks and avoid including them in summaries of small sizes. To address the redundancy problem, NeATS uses a simplified version of CMU's MMR (Goldstein et al., 1999) algorithm. A sentence is added to the summary if and only if its content has less than X percent overlap with the summary.</Paragraph>
    <Paragraph position="2"> Content Presentation To ensure coherence of the summary, NeATS pairs each sentence with an introduction sentence. It then outputs the final sentences in their chronological order.</Paragraph>
  </Section>
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