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<Paper uid="W04-3216">
  <Title>A Phrase-Based HMM Approach to Document/Abstract Alignment</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We describe a model for creating word-to-word and phrase-to-phrase alignments between documents and their human written abstracts. Such alignments are critical for the development of statistical summarization systems that can be trained on large corpora of document/abstract pairs. Our model, which is based on a novel Phrase-Based HMM, outperforms both the Cut &amp; Paste alignment model (Jing, 2002) and models developed in the context of machine translation (Brown et al., 1993).</Paragraph>
  </Section>
class="xml-element"></Paper>