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<Paper uid="P06-1003">
  <Title>Unsupervised Topic Modelling for Multi-Party Spoken Discourse</Title>
  <Section position="4" start_page="0" end_page="17" type="intro">
    <SectionTitle>
2 Background and Related Work
</SectionTitle>
    <Paragraph position="0"> Inthispaperweareinterestedinspokendiscourse, and in particular multi-party human-human meetings. Our overall aim is to produce information which can be used to summarize, browse and/or retrieve the information contained in meetings.</Paragraph>
    <Paragraph position="1"> User studies (Lisowska et al., 2004; Banerjee et al., 2005) have shown that topic information is important here: people are likely to want to know  which topics were discussed in a particular meeting, as well as have access to the discussion on particular topics in which they are interested. Of course, this requires both identification of the topics discussed, and segmentation into the periods of topically related discussion.</Paragraph>
    <Paragraph position="2"> Work on automatic topic segmentation of text and monologue has been prolific, with a variety of approaches used. (Hearst, 1994) uses a measure of lexical cohesion between adjoining paragraphs in text; (Reynar, 1999) and (Beeferman et al., 1999) combine a variety of features such as statistical language modelling, cue phrases, discourse information and the presence of pronouns or named entities to segment broadcast news; (Maskey and Hirschberg, 2003) use entirely non-lexical features. Recent advances have used generative models, allowing lexical models of the topics themselves to be built while segmenting (Imai et al., 1997; Barzilay and Lee, 2004), and we take a similar approach here, although with some important differences detailed below.</Paragraph>
    <Paragraph position="3"> Turning to multi-party discourse and meetings, however, most previous work on automatic segmentation (Reiter and Rigoll, 2004; Dielmann and Renals, 2004; Banerjee and Rudnicky, 2004), treats segments as representing meeting phases or events which characterize the type or style of discourse taking place (presentation, briefing, discussion etc.), rather than the topic or subject matter. While we expect some correlation between these two types of segmentation, they are clearly different problems. However, one comparable study is described in (Galley et al., 2003). Here, a lexical cohesion approach was used to develop an essentially unsupervised segmentation tool (LC-Seg) which was applied to both text and meetingtranscripts, givingperformancebetterthanthat achieved by applying text/monologue-based techniques (see Section 4 below), and we take this as our benchmark for the segmentation problem.</Paragraph>
    <Paragraph position="4"> Note that they improved their accuracy by combining the unsupervised output with discourse features in a supervised classifier - while we do not attempt a similar comparison here, we expect a similar technique would yield similar segmentation improvements.</Paragraph>
    <Paragraph position="5"> In contrast, we take a generative approach, modelling the text as being generated by a sequence of mixtures of underlying topics. The approach is unsupervised, allowing both segmentation and topic extraction from unlabelled data.</Paragraph>
  </Section>
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