<?xml version="1.0" standalone="yes"?>
<Paper uid="W00-1110">
  <Title>Automatic summarization of search engine hit lists</Title>
  <Section position="10" start_page="107" end_page="108" type="concl">
    <SectionTitle>
8 Conclusion and Future Work
</SectionTitle>
    <Paragraph position="0"> We described in this paper a prototype system SNS, which integrates natural language processing and information retrieval techniques to perform automatic customized summarization of search engine results. The user interface and detailed design of SNS's components are also discussed. Task-based extrinsic evaluation showed that the system is of reasonably high quality.</Paragraph>
    <Paragraph position="1"> The following issues will be addressed in the future.</Paragraph>
    <Section position="1" start_page="107" end_page="108" type="sub_section">
      <SectionTitle>
8.1 Interaction between sentence inclusion
</SectionTitle>
      <Paragraph position="0"> in a summary There are two types of interaction (or reinforcement) between sentences in a summary: negative and positive.</Paragraph>
      <Paragraph position="1"> Negative interaction occurs when the inclusion of one sentence in the summary indicates that another sentence should not appear in the summary. This is particularly relevant to multi-document summarization as in this case: negative interaction models the non-inclusion of redundant information.</Paragraph>
      <Paragraph position="2"> The case of positive interaction involves positive reinforcement between sentences. For example, if a sentence with a referring expression is to be  included in a stma~lary, typically the sentence containing the antecedent should also be added. We will investigate specific setups in which positive and/or negative reinforcement between sentences is practical and useful.</Paragraph>
    </Section>
    <Section position="2" start_page="108" end_page="108" type="sub_section">
      <SectionTitle>
8.2 Personalization
</SectionTitle>
      <Paragraph position="0"> We will investigate additional techniques for producing personalized summaries. Some of the approaches that we are considering are: Query words: favoring sentences that include words from the user query in the Web-based scenario Personal preferences and interaction history: we would favor sentences that match the user profile (e.g., overlapping with his or her long-term interests and/or recent queries logged by the system).</Paragraph>
    </Section>
    <Section position="3" start_page="108" end_page="108" type="sub_section">
      <SectionTitle>
8.3 Technical limitations
</SectionTitle>
      <Paragraph position="0"> The current version of our system uses a fairly basic sentence delimiting component. We will investigate the user of robust sentence boundary identification modules in the future.</Paragraph>
      <Paragraph position="1"> We will also investigate the possibility of some limited-form anaphora resolution component.</Paragraph>
    </Section>
    <Section position="4" start_page="108" end_page="108" type="sub_section">
      <SectionTitle>
8.4 Availability
</SectionTitle>
      <Paragraph position="0"> A demonstration version of SNS is available at the following UP.L: http://www.si.umich.edu/-radev/ssearch/</Paragraph>
    </Section>
  </Section>
class="xml-element"></Paper>