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<Paper uid="H05-1032">
  <Title>Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 249-256, Vancouver, October 2005. c(c)2005 Association for Computational Linguistics Bayesian Learning in Text Summarization</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Consider figure 1. What is shown there is the proportion of the times that sentences at particular locations are judged as relevant to summarization, or worthy of inclusion in a summary. Each panel shows judgment results on 25 Japanese texts of a particular genre; columns (G1K3), editorials (G2K3) and news stories (G3K3). All the documents are from a single Japanese news paper, and judgments are elicited from some 100 undergraduate students. While more will be given on the details of the data later (Section 3.2), we can safely ignore them here.</Paragraph>
    <Paragraph position="1"> Each panel has the horizontal axis representing location or order of sentence in a document, and the vertical axis the proportion of the times sentences at particular locations are picked as relevant to summarization. Thus in G1K3, we see that the first sentence (to appear in a document) gets voted for about 12% of the time, while the 26th sentence is voted for less than 2% of the time.</Paragraph>
    <Paragraph position="2"> Curiously enough, each of the panels exhibits a distinct pattern in the way votes are spread across a document: G1K3 has the distribution of votes (DOV) with sharp peaks around 1 and 14; in G2K3, the distribution is peaked around 1, with a small bump around 19; in G3K3, the distribution is sharply skewed to the left, indicating that the majority of votes went to the initial section of a document. What is interesting about the DOV is that we could take it as indicating a collective preference for what to extract for a summary. A question is then, can we somehow exploit the DOV in summarization? To our knowledge, no prior work seems to exist that addresses the question. The paper discusses how we could do this under a Bayesian modeling framework, where we explicitly represent and make use of the DOV by way of Dirichlet posterior (Congdon,</Paragraph>
  </Section>
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