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<Paper uid="P96-1028">
  <Title>Evaluating the Portability of Revision Rules for Incremental Summary Generation</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> The project STREAK 1 focuses on the specific issues involved in generating short, newswire style, natural language texts that summarize vast amount of input tabular data in their historical context. A series of previous publications presented complementary aspects of this project: motivating corpus analysis in (Robin and McKeown, 1993), new revision-based text generation model in (Robin, 1993), system implementation and rule base in (Robin, 1994a) and empirical evaluation of the robustness and scalability of this new model as compared to the traditional single pass pipeline model in (Robin and McKeown, 1995). The present paper completes this series by describing a second, empirical, corpus-based evaluation, this time quantifying the portability to another domain (the stock market) of the revision rule hierarchy acquired in the sports domain and implemented in STREAK. The goal of this paper is twofold:  (1) assessing the generality of this particular rule hierarchy and (2) providing a general, semi-automatic 1 Surface Text Reviser Expressing Additional  Knowledge.</Paragraph>
    <Paragraph position="1"> methodology for evaluating the portability of semantic and syntactic knowledge structures used for natural language generation. The results reveal that at least 59% of the revision rule hierarchy abstracted from the sports domain could also be used to incrementally generate the complex sentences observed in a corpus of stock market reports.</Paragraph>
    <Paragraph position="2"> I start by providing the context of the evaluation with a brief overview of STREAK's revision-based generation model, followed by some details about the empirical acquisition of its revision rules from corpus data. I then present the methodology of this evaluation, followed by a discussion of its quantitative results. Finally, I compare this evaluation with other empirical evaluations in text generation and conclude by discussing future directions.</Paragraph>
  </Section>
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