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<Paper uid="W00-0401">
  <Title>Concept Identification and Presentation in the Context of Technical Text Summarization</Title>
  <Section position="10" start_page="7" end_page="8" type="evalu">
    <SectionTitle>
7.2 Results
</SectionTitle>
    <Paragraph position="0"> For each abstract, we computed the average quality using the scores given by the judges.</Paragraph>
    <Paragraph position="1"> We considered that the abstract indicated the essential content of the source document if two or more judges were able to chose the correct list of keywords for the abstract. The results for individual articles and the average information  are shown in Table 4. For a given source document and type of abstract, the value in column 'Indic?' contains the value 'yes' if the majority of the evaluator have chosen the source document list of keywords for the abstract and 'no' on the contrary. The value in column 'Quality' is the average acceptability for the abstract. Content: In 80% of the cases, the abstracts published with the source documents were correctly classified by the evaluators. Instead, the automatic abstracts were correctly classified in 70% of the cases. It is worth noting &amp;quot;that the automatic systems did not use the * journal abstracts nor the lists of keywords or the.information about the journal.</Paragraph>
    <Paragraph position="2"> Quality: The figures about text acceptability indicate that the abstracts produced by Microsoft'97 Summarizer are below the acceptabil!ty level of 2.5, the abstracts produced by our method are above the acceptability level of 2.5 and that the human abstracts are highly acceptable.</Paragraph>
    <Paragraph position="3"> In a run of this experiment using 30 abstracts from a different set of 10 articles and 15 judges from \]~cole de Biblioth6conomie et des Sciences de l'Information (EBSI) at Universit6 de Montr@al we have obtained similar results (last row in Table 4).</Paragraph>
  </Section>
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