Search (33 results, page 1 of 2)

  • × theme_ss:"Automatisches Abstracting"
  1. Jiang, Y.; Meng, R.; Huang, Y.; Lu, W.; Liu, J.: Generating keyphrases for readers : a controllable keyphrase generation framework (2023) 0.03
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    Date
    22. 6.2023 14:55:20
  2. 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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    Date
    15. 8.1998 20:00:41
    20.10.2000 20:47:10
  3. Endres-Niggemeyer, B.: SimSum : an empirically founded simulation of summarizing (2000) 0.02
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    Date
    15. 8.2002 18:26:20
  4. Goh, A.; Hui, S.C.: TES: a text extraction system (1996) 0.01
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    Date
    26. 2.1997 10:22:43
  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. 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
  7. 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
    2.11.1996 12:46:20
  8. 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
    15. 8.1998 20:00:41
  9. Marsh, E.: ¬A production rule system for message summarisation (1984) 0.01
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    Date
    15. 8.1998 20:00:41
  10. 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.
  11. 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
  12. 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
  13. 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
  14. Craven, T.C.: ¬A computer-aided abstracting tool kit (1993) 0.01
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    Source
    Canadian journal of information and library science. 18(1993) no.2, S.20-31
  15. Craven, T.C.: ¬A phrase flipper for the assistance of writers of abstracts and other text (1995) 0.01
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    Source
    Canadian journal of information and library science. 20(1995) nos.3/4, S.41-49
  16. Summarising software for publishing (1996) 0.01
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    Source
    Digital publisher. 1(1996) no.4, S.15-20
  17. Endres-Niggemeyer, B.: Bessere Information durch Zusammenfassen aus dem WWW (1999) 0.01
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    Date
    9. 7.2011 20:00:41
  18. Ou, S.; Khoo, C.S.G.; Goh, D.H.: Multi-document summarization of news articles using an event-based framework (2006) 0.01
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    Date
    12. 3.2001 11:34:20
    20. 1.2007 11:55:12
  19. Ling, X.; Jiang, J.; He, X.; Mei, Q.; Zhai, C.; Schatz, B.: Generating gene summaries from biomedical literature : a study of semi-structured summarization (2007) 0.01
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    Abstract
    Most knowledge accumulated through scientific discoveries in genomics and related biomedical disciplines is buried in the vast amount of biomedical literature. Since understanding gene regulations is fundamental to biomedical research, summarizing all the existing knowledge about a gene based on literature is highly desirable to help biologists digest the literature. In this paper, we present a study of methods for automatically generating gene summaries from biomedical literature. Unlike most existing work on automatic text summarization, in which the generated summary is often a list of extracted sentences, we propose to generate a semi-structured summary which consists of sentences covering specific semantic aspects of a gene. Such a semi-structured summary is more appropriate for describing genes and poses special challenges for automatic text summarization. We propose a two-stage approach to generate such a summary for a given gene - first retrieving articles about a gene and then extracting sentences for each specified semantic aspect. We address the issue of gene name variation in the first stage and propose several different methods for sentence extraction in the second stage. We evaluate the proposed methods using a test set with 20 genes. Experiment results show that the proposed methods can generate useful semi-structured gene summaries automatically from biomedical literature, and our proposed methods outperform general purpose summarization methods. Among all the proposed methods for sentence extraction, a probabilistic language modeling approach that models gene context performs the best.
    Date
    26.12.2007 16:34:20
  20. Salton, G.: Automatic text structuring and summarization (1997) 0.01
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    Date
    31. 1.1997 20:05:41

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