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<Paper uid="X98-1028">
  <Title>A Text-Extraction Based Summarizer</Title>
  <Section position="4" start_page="229" end_page="229" type="concl">
    <SectionTitle>
CONCLUSIONS
</SectionTitle>
    <Paragraph position="0"> We have developed a method to derive quick-read summaries from news-like texts using a number of shallow NLP techniques and simple quantitative methods. In our approach, a summary is assembled out of passages extracted from the original text, based on a pre-determined Background-News discourse template. The result is a very efficient, robust, and portable summarizer that can be applied to a variety of tasks. These include brief indicative summaries, both generic and topical, as well as longer informative digests. Our method has been shown to produce summaries that offer an excellent tradeoff between text reduction and content preservation, as indicated by the results of the government-sponsored formal evaluation.</Paragraph>
    <Paragraph position="1"> The present version of the summarizer can handle most written texts with well-defined paragraph structure. While the algorithm is primarily tuned to newspaper-like articles, we believe it can produce news-style summaries for other factual texts, as long as their rhetorical structures are reasonably linear, and no prescribed stylistic organization is expected.</Paragraph>
    <Paragraph position="2"> For such cases a more advanced discourse analysis will be required along with more elaborate DMS templates.</Paragraph>
    <Paragraph position="3"> We used the summarizer to build effective search topics for an information retrieval system. This has been demonstrated to produce dramatic performance improvements in TREC evaluations. We believe that this topic expansion approach will also prove useful in searching very large databases where obtaining a full index may be impractical or impossible, and accurate sampling will become critical. Our future development plans will focus on improving the quality of the summaries by implementing additional passage scoring functions. Further plans include handling more complex DMS's, and adaptation of the summarizer to texts other than news, as well as to texts written in foreign languages. We plan further experiments with topic expansion with the goal of achieving a full automation of the process while retaining the performance gains.</Paragraph>
  </Section>
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