Search (29 results, page 1 of 2)

  • × theme_ss:"Automatisches Abstracting"
  1. Salton, G.; Allan, J.; Buckley, C.; Singhal, A.: Automatic analysis, theme generation, and summarization of machine readable texts (1994) 0.03
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    Date
    16. 8.1998 12:30:29
    Footnote
    Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.478-483.
  2. Robin, J.; McKeown, K.: Empirically designing and evaluating a new revision-based model for summary generation (1996) 0.02
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    Date
    6. 3.1997 16:22:15
  3. Kim, H.H.; Kim, Y.H.: Generic speech summarization of transcribed lecture videos : using tags and their semantic relations (2016) 0.01
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    Date
    22. 1.2016 12:29:41
  4. Pinto, M.: Engineering the production of meta-information : the abstracting concern (2003) 0.01
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    Date
    27.11.2005 18:29:55
    Source
    Journal of information science. 29(2003) no.5, S.405-418
  5. Automatic summarizing : introduction (1995) 0.01
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    Content
    Enthält u.a. Beiträge von: J. BATEMAN u. E. TEICH; R. BRANDOW, K. MITZE u. L.F. RAU; B. ENDRES-NIGGEMEYER, E. MAIER u. A. SIGEL; M.T. MAYBURY; K. McKEOWN, J. ROBIN u. K. KUKICH; A. ROTHKEGEL
    Editor
    Sparck Jones, K. u. B. Endres-Niggemeyer
  6. McKeown, K.; Robin, J.; Kukich, K.: Generating concise natural language summaries (1995) 0.01
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  7. Johnson, F.C.; Paice, C.D.; Black, W.J.; Neal, A.P.: ¬The application of linguistic processing to automatic abstract generation (1993) 0.01
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    Footnote
    Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.538-552.
  8. Marsh, E.: ¬A production rule system for message summarisation (1984) 0.01
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    Footnote
    Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.534-537.
  9. Brandow, R.; Mitze, K.; Rau, L.F.: Automatic condensation of electronic publications by sentence selection (1995) 0.01
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  10. Sparck Jones, K.; Endres-Niggemeyer, B.: Introduction: automatic summarizing (1995) 0.01
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  11. Ahmad, K.: Text summarisation : the role of lexical cohesion analysis (1995) 0.01
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  12. Gomez, J.; Allen, K.; Matney, M.; Awopetu, T.; Shafer, S.: Experimenting with a machine generated annotations pipeline (2020) 0.01
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  13. 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
  14. Goh, A.; Hui, S.C.: TES: a text extraction system (1996) 0.01
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    Date
    26. 2.1997 10:22:43
  15. 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
  16. 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
  17. Lam, W.; Chan, K.; Radev, D.; Saggion, H.; Teufel, S.: Context-based generic cross-lingual retrieval of documents and automated summaries (2005) 0.00
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  18. Sparck Jones, K.: Automatic summarising : the state of the art (2007) 0.00
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  19. Nomoto, T.: Discriminative sentence compression with conditional random fields (2007) 0.00
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    Abstract
    The paper focuses on a particular approach to automatic sentence compression which makes use of a discriminative sequence classifier known as Conditional Random Fields (CRF). We devise several features for CRF that allow it to incorporate information on nonlinear relations among words. Along with that, we address the issue of data paucity by collecting data from RSS feeds available on the Internet, and turning them into training data for use with CRF, drawing on techniques from biology and information retrieval. We also discuss a recursive application of CRF on the syntactic structure of a sentence as a way of improving the readability of the compression it generates. Experiments found that our approach works reasonably well compared to the state-of-the-art system [Knight, K., & Marcu, D. (2002). Summarization beyond sentence extraction: A probabilistic approach to sentence compression. Artificial Intelligence 139, 91-107.].
  20. Steinberger, J.; Poesio, M.; Kabadjov, M.A.; Jezek, K.: Two uses of anaphora resolution in summarization (2007) 0.00
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