<?xml version="1.0" standalone="yes"?>
<Paper uid="W00-0405">
  <Title>Multi-Document Summarization By Sentence Extraction</Title>
  <Section position="8" start_page="45" end_page="46" type="concl">
    <SectionTitle>
7 Conclusions and Future Work
</SectionTitle>
    <Paragraph position="0"> This paper presented a statistical method of generating extraction based multi-document summaries. It builds upon previous work in single-document summarization and takes into account some of the major differences between single-document and multi-document summarization: (i) the need to carefully eliminate redundant information from multiple documents, and achieve high compression ratios, (ii) take into account information about document and passage similarities, and weight different passages accordingly, and (iii) take temporal information into account.</Paragraph>
    <Paragraph position="1"> Our approach differs from others in several ways: it is completely domain-independent, is based mainly on fast, statistical processing, it attempts to maximize the novelty of the information being selected, and different  genres or corpora characteristics can be taken into account easily. Since our system is not based on the use of sophisticated natural language understanding or information extraction techniques, summaries lack co-reference resolution, passages may be disjoint from one another, and in some cases may have false implicature.</Paragraph>
    <Paragraph position="2"> In future work, we will integrate work on multi-document summarization with work on clustering to provide summaries for clusters produced by topic detection and tracking. We also plan to investigate how to generate coherent temporally based event summaries. We will also investigate how users can effectively use multi-document summarization through interactive interfaces to browse and explore large document sets.</Paragraph>
  </Section>
class="xml-element"></Paper>