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<Paper uid="W90-0111">
  <Title>Selection: Salience, Relevance and the Coupling between Domain-Level Tasks and Text Planning</Title>
  <Section position="3" start_page="0" end_page="79" type="intro">
    <SectionTitle>
1. Introduction
</SectionTitle>
    <Paragraph position="0"> Most models of natural language generation, be they computational or psychological, recognize that the task of text planning (also called conceptnalization\[14\], \[10\]), comprises the following essential subtasks: (1) content selection and (2) content organization. The task of content selection (hereafter, selection) is concerned with computing, or retrieving from a knowledge base, the primary content of texts. Selection may also include a phase of content expansion, also called additional topic inclusion \[8\]. Content expansion consists in inferentially generating or selecting additional material for expression in text, once the primary material is selected. The task of content organization, also called topic organization \[8\], content ordering or linearization \[13\], \[14\] deals with ordering the chosen content into a sequence of propositional (linear) representations, appropriate for realization into coherent text.</Paragraph>
    <Paragraph position="1"> Many text planning models concentrate chiefly on content organization and porhaps content expansion. Such planners accept as input a pre-selected, and to some extent, pre-sequenced collection of representations that serve as the raw material of text content. The knowledge base from which these input elements are selected, as well as the selection process itself, are treated to be external to the text planner. In such systems, whatever selection is porformed by the text planner is confined to the task of choosing a subset of the input elements according to control exercised by knowledge sources resident within the text planner.</Paragraph>
    <Paragraph position="2"> Other examples of systems in which pre-selected content is input to the text planner include natural language generation front-ends to export systems, database systems or other application programs- in other words, a problemsolver or a host system which is devoted to domain-level non-linguistic activities.</Paragraph>
    <Paragraph position="3"> In some other models, such as those of Paris \[23\], the knowledge base from which much of the text content is drawn is resident within the text generation system itself.</Paragraph>
    <Paragraph position="4"> Selection in such systems is totally a responsibility of the text planner. In the TAILOR system of \[23\], for instance, facts describing objects are stored in a knowledge base, and the textual component selects the content of the description from the knowledge base under the regulatory influence of a user model. The urge to generate text is input to the generator in the form of a request for the definition of an object. In all cases, the beholder of the natural language output sees the text as coming from a single program which can porform domain-level tasks as well as text production tasks: the speaker and the thinker are one and the same. If the beholder were a text planning researcher, she could be inclined to pose questions about the origin of text content.</Paragraph>
    <Paragraph position="5"> Our recent research has been motivated and directed by the adoption of such a role. The problem of selection in texts (in our case, multisentential descriptive texts) led us to examine the nature of input to the text planner, and the boundary between the domain-level program and the text planner. When we regarded the thinker and the speaker as a unified whole, we were led to search for very general factors that influenced selection in diverse domains of discourse production. When we viewed domain-level activities and text planning activities as distinct tasks, we examined the division of duties between the text planner and its underlying program in the task of selection, and explored the conditions under which the modular boundary between the domain-level and the text-planning level could be kept intact and those under which it might break down.</Paragraph>
    <Paragraph position="6"> This papor is devoted to a presentation of our research on some issues portinent to selection in text planning. We found that the fundamental notions that were crucial to understanding selection in text planning were salience and relevance. These are not altogether unfamiliar notions.</Paragraph>
    <Paragraph position="7"> Conklin and McDonald \[2\] and Waltz \[33\] describe some effects of salience in generating scene descriptions.</Paragraph>
    <Paragraph position="8"> Researchers in natural language understanding have studied salience and relevance in some detail and have used  them profitably in their accounts. However, in the natural language generation research community, the terms are used in their literal sense, and often interchangeably, whereas in fact they are distinct notions. In this paper we present the notions of salience and relevance as they pertain to natural language generation, in particular, to (content) selection.</Paragraph>
    <Paragraph position="9"> Our presentation in section 2 is a synthesis of several analyses of salience and relevance in the disciplines of language understanding, psycholinguistics and communication. In section 3 we consider the coupling between domain-level tasks and text planning tasks from the point of view of selection. Our work in the domain of route description generation is presented in section 4. In this domain, interesting questions emer!~e regarding mode of knowledge representation, connecUons between text planning and domain-level problem solving, and selection.</Paragraph>
    <Paragraph position="10"> In the concluding section we briefly state our current work and research plans for the near future.</Paragraph>
  </Section>
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