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<Paper uid="W00-1110">
  <Title>Automatic summarization of search engine hit lists</Title>
  <Section position="9" start_page="107" end_page="107" type="relat">
    <SectionTitle>
7 Related Work
</SectionTitle>
    <Paragraph position="0"> Neto et al. (2000) describes a text mining tool that performs document clustering and text summarization. They used the Autoclass algorithm to perform document clustering and used TF-ISF (an adaptation of TF-IDF) to perform sentence ranking and generate the summarization output. Our work is different from theirs in that we perform personalized summarization based on the retrieval result from a generic personalized web-based search engine.</Paragraph>
    <Paragraph position="1"> A more complicated sentence ranking functions is employed to boost the ranking performance.</Paragraph>
    <Paragraph position="2"> The compression ratio for the summary is customizable by a user. Both single-document for a single URL and multiple-document i Since the length of the summary is only 20% of the original documents, the maximum speedup in terms of reading time is 1/0.2=5.</Paragraph>
    <Paragraph position="3"> summarization for a cluster of URLs are supported in our system.</Paragraph>
    <Paragraph position="4"> More related work can be found in Extractor web site http'J/extractor.iit.nrc.ca/. They use MetaCrawler to perform web-based search and automatically generate summaries for each URLs retrieved. They only support single document summarization in their engine and the compression rate of the summarizer is also noncustomizable. We not only support both single and multiple document summarization, but also allow the user to specify the summarization compression ratio as well as to get per-cluster summaries of automatically generated clusters, which, we believe, are more valuable to online users and give them more flexibility and control of the surnrnarization results.</Paragraph>
  </Section>
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