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<Paper uid="W05-1628">
  <Title>2Information and Communication Technologies</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> In many text-to-text generation scenarios (for instance, summarisation), we encounter human-authored sentences that could be composed by recycling portions of related sentences to form new sentences. In this paper, we couch the generation of such sentences as a search problem. We investigate a statistical sentence generation method which recombines words to form new sentences.</Paragraph>
    <Paragraph position="1"> We propose an extension to the Viterbi algorithm designed to improve the grammaticality of generated sentences. Within a statistical framework, the extension favours those partially generated strings with a probable dependency tree structure. Our preliminary evaluations show that our approach generates less fragmented text than a bigram baseline. null</Paragraph>
  </Section>
class="xml-element"></Paper>