Search (116 results, page 1 of 6)

  • × theme_ss:"Automatisches Abstracting"
  1. Robin, J.; McKeown, K.: Empirically designing and evaluating a new revision-based model for summary generation (1996) 0.05
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    Date
    6. 3.1997 16:22:15
    Source
    Artificial intelligence. 85(1996) nos.1/2, S.135-179
  2. Automatic summarizing : introduction (1995) 0.04
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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
    Source
    Information processing and management. 31(1995) no.5, S.625-630
    Type
    s
  3. McKeown, K.; Robin, J.; Kukich, K.: Generating concise natural language summaries (1995) 0.03
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    Source
    Information processing and management. 31(1995) no.5, S.703-733
  4. Johnson, F.C.; Paice, C.D.; Black, W.J.; Neal, A.P.: ¬The application of linguistic processing to automatic abstract generation (1993) 0.02
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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.
    Source
    Journal of document and text management. 1(1993), S.215-241
  5. Salton, G.; Allan, J.; Buckley, C.; Singhal, A.: Automatic analysis, theme generation, and summarization of machine readable texts (1994) 0.02
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    Footnote
    Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.478-483.
    Source
    Science. 264(1994), S.1421-1426
  6. Marsh, E.: ¬A production rule system for message summarisation (1984) 0.02
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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.
    Pages
    S.243-246
  7. Brandow, R.; Mitze, K.; Rau, L.F.: Automatic condensation of electronic publications by sentence selection (1995) 0.02
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    Source
    Information processing and management. 31(1995) no.5, S.675-685
  8. Sparck Jones, K.; Endres-Niggemeyer, B.: Introduction: automatic summarizing (1995) 0.02
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    Source
    Information processing and management. 31(1995) no.5, S.625-630
  9. Ahmad, K.: Text summarisation : the role of lexical cohesion analysis (1995) 0.02
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    Source
    New review of document and text management. 1995, no.1, S.321-335
  10. Gomez, J.; Allen, K.; Matney, M.; Awopetu, T.; Shafer, S.: Experimenting with a machine generated annotations pipeline (2020) 0.02
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  11. Goh, A.; Hui, S.C.: TES: a text extraction system (1996) 0.02
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    Date
    26. 2.1997 10:22:43
    Source
    Microcomputers for information management. 13(1996) no.1, S.41-55
  12. Jones, P.A.; Bradbeer, P.V.G.: Discovery of optimal weights in a concept selection system (1996) 0.02
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    Pages
    S.145-153
    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
  13. Lam, W.; Chan, K.; Radev, D.; Saggion, H.; Teufel, S.: Context-based generic cross-lingual retrieval of documents and automated summaries (2005) 0.01
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    Source
    Journal of the American Society for Information Science and Technology. 56(2005) no.2, S.129-139
  14. Sparck Jones, K.: Automatic summarising : the state of the art (2007) 0.01
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    Source
    Information processing and management. 43(2007) no.6, S.1449-1481
  15. Nomoto, T.: Discriminative sentence compression with conditional random fields (2007) 0.01
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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.].
    Source
    Information processing and management. 43(2007) no.6, S.1571-1587
  16. Steinberger, J.; Poesio, M.; Kabadjov, M.A.; Jezek, K.: Two uses of anaphora resolution in summarization (2007) 0.01
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    Source
    Information processing and management. 43(2007) no.6, S.1663-1680
  17. Vanderwende, L.; Suzuki, H.; Brockett, J.M.; Nenkova, A.: Beyond SumBasic : task-focused summarization with sentence simplification and lexical expansion (2007) 0.01
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    Abstract
    In recent years, there has been increased interest in topic-focused multi-document summarization. In this task, automatic summaries are produced in response to a specific information request, or topic, stated by the user. The system we have designed to accomplish this task comprises four main components: a generic extractive summarization system, a topic-focusing component, sentence simplification, and lexical expansion of topic words. This paper details each of these components, together with experiments designed to quantify their individual contributions. We include an analysis of our results on two large datasets commonly used to evaluate task-focused summarization, the DUC2005 and DUC2006 datasets, using automatic metrics. Additionally, we include an analysis of our results on the DUC2006 task according to human evaluation metrics. In the human evaluation of system summaries compared to human summaries, i.e., the Pyramid method, our system ranked first out of 22 systems in terms of overall mean Pyramid score; and in the human evaluation of summary responsiveness to the topic, our system ranked third out of 35 systems.
    Source
    Information processing and management. 43(2007) no.6, S.1606-1618
  18. Sankarasubramaniam, Y.; Ramanathan, K.; Ghosh, S.: Text summarization using Wikipedia (2014) 0.01
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    Source
    Information processing and management. 50(2014) no.3, S.443-461
  19. Oh, H.; Nam, S.; Zhu, Y.: Structured abstract summarization of scientific articles : summarization using full-text section information (2023) 0.01
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    Date
    22. 1.2023 18:57:12
    Source
    Journal of the Association for Information Science and Technology. 74(2023) no.2, S.234-248
  20. Wu, Y.-f.B.; Li, Q.; Bot, R.S.; Chen, X.: Finding nuggets in documents : a machine learning approach (2006) 0.01
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    Date
    22. 7.2006 17:25:48
    Source
    Journal of the American Society for Information Science and Technology. 57(2006) no.6, S.740-752

Years

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Types

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  • m 5
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