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<Paper uid="C82-1013">
  <Title>FORWARD AND BACKWARD REASONING IN AUTOMATIC ABSTRACTING</Title>
  <Section position="6" start_page="83" end_page="83" type="evalu">
    <SectionTitle>
FORWARD AND BACKWARD REASONING IN AUTOMATIC ABSTRACTING 8?
</SectionTitle>
    <Paragraph position="0"> The input text in this example is a slightly adapted version ~f the first sentence of an article entitled &amp;quot;Fast Breeder Reactors&amp;quot; taken from Meyer (1975). &amp;quot;The need to generate enormous additional amounts of electric power while at the same time protecting the environment is one of the major social and technological problems that our society must solve in the next future.&amp;quot; The parser maps this text into the internal representation:  The internal representation is then passed to the weighter in order to attach. following the suggestions contained in the text schema, an integer weigth to each proposition. As a result the weighted representation is obtained, which is graphically expressed as a network:</Paragraph>
    <Paragraph position="2"> We mention here the three most relevant rules applied by the weighter to generate this network: W.RULEI. IF a proposition i is referred to by a different proposition j, THEN assign weigths w such as w(i) &lt; w(j).</Paragraph>
    <Paragraph position="3"> W.RULE2. IF the predicate of a proposition i is constituted by a modifier AND (the proposition i is referred to by a proposition j OR the proposition i has among its arguments one which has already appeared in a preceding proposition j) THEN assign weigths w such as w(j)&lt; w(i).</Paragraph>
    <Paragraph position="4"> W.RULE3. IF a proposition i has among its arguments one which has already appeared in a preceding proposition j AND W. RULE2 is not applied. THEN a~sign weigths w such as ~i)&lt; w(j).</Paragraph>
    <Paragraph position="5"> Let us note that modifiers in our approach are constituted by types that 88 D. FL~, G. GUIDA md C. TASSO grammatically can be classified as adjectives or adverbials, and types such as TIME OF, QUANTITY OF, LOCATIVE OF and so on.</Paragraph>
    <Paragraph position="6"> The weighted representation is then supplied to the selector which chooses a certain number of PrOPOsitions that will constitute the selected internal representation of the stmmmry and will be passed to the generator in order to produce the final output summary. This choice is driven by the specifications contained in the summary schema.</Paragraph>
    <Paragraph position="7"> In our example, through the application of the selecting rule: S.RULEI Choose the n most weighted propositions discarding the leaves.</Paragraph>
    <Paragraph position="8"> where n is a parameter which takes into account the length of the desired summary (in the example, n=5), we can select the propositions that appear encircled in the network.</Paragraph>
    <Paragraph position="9"> These propositions are eventually passed to the generator which gives the final output summary: &amp;quot;The society must solve in the future the problem of the need to generate power while protecting the environment.&amp;quot; The specifications given by the user through the text schema and the summary schema may of course activate different weighting and selecting rules- and thus generate different summaries.</Paragraph>
  </Section>
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