Search (106 results, page 3 of 6)

  • × theme_ss:"Automatisches Abstracting"
  • × type_ss:"a"
  1. Johnson, F.: Automatic abstracting research (1995) 0.00
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    Source
    Library review. 44(1995) no.8, S.28-36
  2. Craven, T.C.: ¬A phrase flipper for the assistance of writers of abstracts and other text (1995) 0.00
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    Source
    Canadian journal of information and library science. 20(1995) nos.3/4, S.41-49
  3. Summarising software for publishing (1996) 0.00
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    Source
    Digital publisher. 1(1996) no.4, S.15-20
  4. Ahmad, K.: Text summarisation : the role of lexical cohesion analysis (1995) 0.00
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    Source
    New review of document and text management. 1995, no.1, S.321-335
  5. Endres-Niggemeyer, B.; Neugebauer, E.: Professional summarizing : no cognitive simulation without observation (1998) 0.00
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    Source
    Journal of the American Society for Information Science. 49(1998) no.6, S.486-506
  6. Xianghao, G.; Yixin, Z.; Li, Y.: ¬A new method of news test understanding and abstracting based on speech acts theory (1998) 0.00
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    Source
    Journal of the China Society for Scientific and Technical Information. 17(1998) no.4, S.257-262
  7. Liu, J.; Wu, Y.; Zhou, L.: ¬A hybrid method for abstracting newspaper articles (1999) 0.00
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    Source
    Journal of the American Society for Information Science. 50(1999) no.13, S.1234-1245
  8. Deutsche Patentdatenbank mit maschinellen Abstract-Übersetzungen (2005) 0.00
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    Source
    Password. 2005, H.2, S.32
  9. Over, P.; Dang, H.; Harman, D.: DUC in context (2007) 0.00
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    Source
    Information processing and management. 43(2007) no.6, S.1506-1520
  10. Sjöbergh, J.: Older versions of the ROUGEeval summarization evaluation system were easier to fool (2007) 0.00
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    Source
    Information processing and management. 43(2007) no.6, S.1500-1505
  11. Gomez, J.; Allen, K.; Matney, M.; Awopetu, T.; Shafer, S.: Experimenting with a machine generated annotations pipeline (2020) 0.00
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  12. Jones, S.; Paynter, G.W.: Automatic extractionof document keyphrases for use in digital libraries : evaluations and applications (2002) 0.00
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    Source
    Journal of the American Society for Information Science and technology. 53(2002) no.8, S.653-677
  13. Endres-Niggemeyer, B.; Jauris-Heipke, S.; Pinsky, S.M.; Ulbricht, U.: Wissen gewinnen durch Wissen : Ontologiebasierte Informationsextraktion (2006) 0.00
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    Source
    Information - Wissenschaft und Praxis. 57(2006) H.6/7, S.301-308
  14. Ou, S.; Khoo, C.S.G.; Goh, D.H.: Multi-document summarization of news articles using an event-based framework (2006) 0.00
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    Source
    Aslib proceedings. 58(2006) no.3, S.276-291
  15. Ou, S.; Khoo, S.G.; Goh, D.H.: Automatic multidocument summarization of research abstracts : design and user evaluation (2007) 0.00
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    Source
    Journal of the American Society for Information Science and Technology. 58(2007) no.10, S.1419-1435
  16. Sweeney, S.; Crestani, F.; Losada, D.E.: 'Show me more' : incremental length summarisation using novelty detection (2008) 0.00
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    Source
    Information processing and management. 44(2008) no.2, S.663-686
  17. Ouyang, Y.; Li, W.; Li, S.; Lu, Q.: Intertopic information mining for query-based summarization (2010) 0.00
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    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.5, S.1062-1072
  18. Sankarasubramaniam, Y.; Ramanathan, K.; Ghosh, S.: Text summarization using Wikipedia (2014) 0.00
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    Source
    Information processing and management. 50(2014) no.3, S.443-461
  19. Yulianti, E.; Huspi, S.; Sanderson, M.: Tweet-biased summarization (2016) 0.00
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    Source
    Journal of the Association for Information Science and Technology. 67(2016) no.6, S.1289-1300
  20. Atanassova, I.; Bertin, M.; Larivière, V.: On the composition of scientific abstracts (2016) 0.00
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    Abstract
    Purpose - Scientific abstracts reproduce only part of the information and the complexity of argumentation in a scientific article. The purpose of this paper provides a first analysis of the similarity between the text of scientific abstracts and the body of articles, using sentences as the basic textual unit. It contributes to the understanding of the structure of abstracts. Design/methodology/approach - Using sentence-based similarity metrics, the authors quantify the phenomenon of text re-use in abstracts and examine the positions of the sentences that are similar to sentences in abstracts in the introduction, methods, results and discussion structure, using a corpus of over 85,000 research articles published in the seven Public Library of Science journals. Findings - The authors provide evidence that 84 percent of abstract have at least one sentence in common with the body of the paper. Studying the distributions of sentences in the body of the articles that are re-used in abstracts, the authors show that there exists a strong relation between the rhetorical structure of articles and the zones that authors re-use when writing abstracts, with sentences mainly coming from the beginning of the introduction and the end of the conclusion. Originality/value - Scientific abstracts contain what is considered by the author(s) as information that best describe documents' content. This is a first study that examines the relation between the contents of abstracts and the rhetorical structure of scientific articles. The work might provide new insight for improving automatic abstracting tools as well as information retrieval approaches, in which text organization and structure are important features.
    Source
    Journal of documentation. 72(2016) no.4, S.636-647

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