Search (28 results, page 1 of 2)

  • × theme_ss:"Automatisches Abstracting"
  1. Dammeyer, A.; Jürgensen, W.; Krüwel, C.; Poliak, E.; Ruttkowski, S.; Schäfer, Th.; Sirava, M.; Hermes, T.: Videoanalyse mit DiVA (1998) 0.02
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    Abstract
    Die Bedeutung von Videos nimmt für multimediale Systeme stetig zu. Dabei existiert eine Vielzahl von Produkten zur Betrachtung von Videos, allerdings nur wenige Ansätze, den Inhalt eines Videos zu erschließen. Das DiVA-System, welches an der Universität Bremen im Rahmen eines studentischen Projektes entwickelt wird, dient der automatischen Analyse von MPEG-I Videofilmen. Der dabei verfolgte Ansatz läßt sich in vier Phasen gliedern. Zunächst wird der Videofilm durch eine Shotanalyse in seine einzelnen Kameraeinstellungen (Shots) unterteilt. Darauf aufbauend findet eine Kamerabewegungsanalyse sowie die Erstellung von Mosaicbildern statt. Mit Methoden der künstlichen Intelligenz und der digitalen Bildverarbeitung wird das analysierte Material nach Bild- und Toninformationen ausgewertet. Das Resultat ist eine textuelle Beschreibung eines Videofilms, auf der mit Hilfe von Text-Retrieval-Systemen recherchiert werden kann
  2. Gomez, J.; Allen, K.; Matney, M.; Awopetu, T.; Shafer, S.: Experimenting with a machine generated annotations pipeline (2020) 0.02
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  3. Nomoto, T.: Discriminative sentence compression with conditional random fields (2007) 0.02
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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.].
  4. Mani, T.: Automatic summarization (2001) 0.01
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  5. 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
  6. Ye, S.; Chua, T.-S.; Kan, M.-Y.; Qiu, L.: Document concept lattice for text understanding and summarization (2007) 0.01
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  7. Hirao, T.; Okumura, M.; Yasuda, N.; Isozaki, H.: Supervised automatic evaluation for summarization with voted regression model (2007) 0.01
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  8. Dorr, B.J.; Gaasterland, T.: Exploiting aspectual features and connecting words for summarization-inspired temporal-relation extraction (2007) 0.01
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  9. Automatic summarizing : introduction (1995) 0.00
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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
  10. McKeown, K.; Robin, J.; Kukich, K.: Generating concise natural language summaries (1995) 0.00
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  11. Goh, A.; Hui, S.C.: TES: a text extraction system (1996) 0.00
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    Date
    26. 2.1997 10:22:43
  12. Jones, P.A.; Bradbeer, P.V.G.: Discovery of optimal weights in a concept selection system (1996) 0.00
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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
  13. Johnson, F.C.; Paice, C.D.; Black, W.J.; Neal, A.P.: ¬The application of linguistic processing to automatic abstract generation (1993) 0.00
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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.
  14. Salton, G.; Allan, J.; Buckley, C.; Singhal, A.: Automatic analysis, theme generation, and summarization of machine readable texts (1994) 0.00
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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.
  15. Marsh, E.: ¬A production rule system for message summarisation (1984) 0.00
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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.
  16. Vanderwende, L.; Suzuki, H.; Brockett, J.M.; Nenkova, A.: Beyond SumBasic : task-focused summarization with sentence simplification and lexical expansion (2007) 0.00
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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.
  17. Brandow, R.; Mitze, K.; Rau, L.F.: Automatic condensation of electronic publications by sentence selection (1995) 0.00
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  18. Sparck Jones, K.; Endres-Niggemeyer, B.: Introduction: automatic summarizing (1995) 0.00
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  19. Ahmad, K.: Text summarisation : the role of lexical cohesion analysis (1995) 0.00
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  20. Wu, Y.-f.B.; Li, Q.; Bot, R.S.; Chen, X.: Finding nuggets in documents : a machine learning approach (2006) 0.00
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    Date
    22. 7.2006 17:25:48