Search (32 results, page 1 of 2)

  • × theme_ss:"Automatisches Abstracting"
  1. Goh, A.; Hui, S.C.: TES: a text extraction system (1996) 0.03 
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    Date
    26. 2.1997 10:22:43
    Source
    Microcomputers for information management. 13(1996) no.1, S.41-55
  2. Robin, J.; McKeown, K.: Empirically designing and evaluating a new revision-based model for summary generation (1996) 0.03 
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    Date
    6. 3.1997 16:22:15
    Source
    Artificial intelligence. 85(1996) nos.1/2, S.135-179
  3. Endres-Niggemeyer, B.: Kognitive Modellierung des Abstracting (1991) 0.01 
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    Series
    DGD-Schrift (DOK-3); 1/91
    Source
    Deutscher Dokumentartag 1990. 1. Deutsch-deutscher Dokumentartag, 25.-27.9.90, Fulda. Proceedings. Hrsg. W. Neubauer u. U. Schneider-Briehn
  4. 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
  5. Kuhlen, R.: Abstracts, abstracting : intellektuelle und maschinelle Verfahren (1990) 0.01 
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    Source
    Grundlagen der praktischen Information und Dokumentation. 3. Aufl. Hrsg.: M. Buder u.a. Bd.1
  6. Edmundson, H.P.; Wyllis, R.E.: Problems in automatic abstracting (1964) 0.01 
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    Source
    Communications of the Association for Computing Machinery. 7(1964) no.1, S.259-263
  7. Mani, T.: Automatic summarization (2001) 0.01 
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    Isbn
    1-58811-060-5
  8. 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.
  9. Johnson, F.C.: ¬A critical view of system-centered to user-centered evaluation of automatic abstracting research (1999) 0.01 
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    Date
    1. 4.2002 13:48:19
  10. 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
  11. 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
  12. 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
  13. Jiang, Y.; Meng, R.; Huang, Y.; Lu, W.; Liu, J.: Generating keyphrases for readers : a controllable keyphrase generation framework (2023) 0.01 
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    Date
    22. 6.2023 14:55:20
  14. 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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    Source
    Journal of document and text management. 1(1993), S.215-241
  15. Advances in automatic text summarization (1999) 0.01 
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    Date
    1. 2.1997 9:16:32
  16. Moens, M.F.: Automatic indexing and abstracting of document texts (2000) 0.01 
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    Isbn
    0-7923-7793-1
  17. Endres-Niggemeyer, B.: Summarizing information (1998) 0.01 
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    Pages
    VII, 375 S. + 1 CD-ROM
    Signature
    79 BCA 129-1 (75 BCA 129-2)
  18. Liang, S.-F.; Devlin, S.; Tait, J.: Investigating sentence weighting components for automatic summarisation (2007) 0.01 
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    Abstract
    The work described here initially formed part of a triangulation exercise to establish the effectiveness of the Query Term Order algorithm. It subsequently proved to be a reliable indicator for summarising English web documents. We utilised the human summaries from the Document Understanding Conference data, and generated queries automatically for testing the QTO algorithm. Six sentence weighting schemes that made use of Query Term Frequency and QTO were constructed to produce system summaries, and this paper explains the process of combining and balancing the weighting components. The summaries produced were evaluated by the ROUGE-1 metric, and the results showed that using QTO in a weighting combination resulted in the best performance. We also found that using a combination of more weighting components always produced improved performance compared to any single weighting component.
    Source
    Information processing and management. 43(2007) no.1, S.146-153
  19. Summarising software for publishing (1996) 0.01 
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    Source
    Digital publisher. 1(1996) no.4, S.15-20
  20. Ahmad, K.: Text summarisation : the role of lexical cohesion analysis (1995) 0.01 
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    Source
    New review of document and text management. 1995, no.1, S.321-335

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