Search (9 results, page 1 of 1)

  • × theme_ss:"Automatisches Abstracting"
  • × year_i:[1990 TO 2000}
  1. McKeown, K.; Robin, J.; Kukich, K.: Generating concise natural language summaries (1995) 0.01
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    Abstract
    Description of the problems for summary generation, the applications developed (for basket ball games - STREAK and for telephone network planning activity - PLANDOC), the linguistic constructions that the systems use to convey information concisely and the textual constraints that determine what information gets included
  2. Craven, T.C.: ¬A computer-aided abstracting tool kit (1993) 0.01
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    Abstract
    Describes the abstracting assistance features being prototyped in the TEXNET text network management system. Sentence weighting methods include: weithing negatively or positively on the stems in a selected passage; weighting on general lists of cue words, adjusting weights of selected segments; and weighting of occurrence of frequent stems. The user may adjust a number of parameters: the minimum strength of extracts; the threshold for frequent word/stems and the amount sentence weight is to be adjusted for each weighting type
  3. Salton, G.; Allan, J.; Buckley, C.; Singhal, A.: Automatic analysis, theme generation, and summarization of machine readable texts (1994) 0.01
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    Date
    16. 8.1998 12:30:29
  4. Moens, M.-F.; Uyttendaele, C.: Automatic text structuring and categorization as a first step in summarizing legal cases (1997) 0.01
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    Abstract
    The SALOMON system automatically summarizes Belgian criminal cases in order to improve access to the large number of existing and future court decisions. SALOMON extracts relevant text units from the case text to form a case summary. Such a case profile facilitates the rapid determination of the relevance of the case or may be employed in text search. In a first important abstracting step SALOMON performs an initial categorization of legal criminal cases and structures the case text into separate legally relevant and irrelevant components. A text grammar represented as a semantic network is used to automatically determine the category of the case and its components. Extracts from the case general data and identifies text portions relevant for further abstracting. Prior knowledge of the text structure and its indicative cues may support automatic abstracting. A text grammar is a promising form for representing the knowledge involved
  5. Craven, T.C.: ¬A phrase flipper for the assistance of writers of abstracts and other text (1995) 0.01
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    Date
    17. 8.1996 10:29:59
  6. Goh, A.; Hui, S.C.: TES: a text extraction system (1996) 0.01
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    Date
    26. 2.1997 10:22:43
  7. Robin, J.; McKeown, K.: Empirically designing and evaluating a new revision-based model for summary generation (1996) 0.01
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    Date
    6. 3.1997 16:22:15
  8. Jones, P.A.; Bradbeer, P.V.G.: Discovery of optimal weights in a concept selection system (1996) 0.01
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    Source
    Information retrieval: new systems and current research. Proceedings of the 16th Research Colloquium of the British Computer Society Information Retrieval Specialist Group, Drymen, Scotland, 22-23 Mar 94. Ed.: R. Leon
  9. Uyttendaele, C.; Moens, M.-F.; Dumortier, J.: SALOMON: automatic abstracting of legal cases for effective access to court decisions (1998) 0.00
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    Date
    17. 7.1996 14:16:29