Search (20 results, page 1 of 1)

  • × year_i:[2010 TO 2020}
  • × theme_ss:"Automatisches Abstracting"
  1. 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
  2. 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
    Source
    Journal of the Association for Information Science and Technology. 67(2016) no.2, S.366-379
  3. Hahn, U.: Automatisches Abstracting (2013) 0.00
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    Pages
    S.286-301
  4. Xiong, S.; Ji, D.: Query-focused multi-document summarization using hypergraph-based ranking (2016) 0.00
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    Source
    Information processing and management. 52(2016) no.4, S.670-681
  5. 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
  6. 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
  7. 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
  8. Wang, S.; Koopman, R.: Embed first, then predict (2019) 0.00
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    Source
    Knowledge organization. 46(2019) no.5, S.364-370
  9. Plaza, L.; Stevenson, M.; Díaz, A.: Resolving ambiguity in biomedical text to improve summarization (2012) 0.00
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    Source
    Information processing and management. 48(2012) no.4, S.755-766
  10. Wang, W.; Hwang, D.: Abstraction Assistant : an automatic text abstraction system (2010) 0.00
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    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.9, S.1790-1799
  11. Abdi, A.; Idris, N.; Alguliev, R.M.; Aliguliyev, R.M.: Automatic summarization assessment through a combination of semantic and syntactic information for intelligent educational systems (2015) 0.00
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    Source
    Information processing and management. 51(2015) no.4, S.340-358
  12. Xu, D.; Cheng, G.; Qu, Y.: Preferences in Wikipedia abstracts : empirical findings and implications for automatic entity summarization (2014) 0.00
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    Source
    Information processing and management. 50(2014) no.2, S.284-296
  13. Martinez-Romo, J.; Araujo, L.; Fernandez, A.D.: SemGraph : extracting keyphrases following a novel semantic graph-based approach (2016) 0.00
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    Source
    Journal of the Association for Information Science and Technology. 67(2016) no.1, S.71-82
  14. Kannan, R.; Ghinea, G.; Swaminathan, S.: What do you wish to see? : A summarization system for movies based on user preferences (2015) 0.00
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    Source
    Information processing and management. 51(2015) no.3, S.286-305
  15. Cai, X.; Li, W.: Enhancing sentence-level clustering with integrated and interactive frameworks for theme-based summarization (2011) 0.00
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    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.10, S.2067-2082
  16. Galgani, F.; Compton, P.; Hoffmann, A.: Summarization based on bi-directional citation analysis (2015) 0.00
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    Source
    Information processing and management. 51(2015) no.1, S.1-24
  17. Finegan-Dollak, C.; Radev, D.R.: Sentence simplification, compression, and disaggregation for summarization of sophisticated documents (2016) 0.00
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    Source
    Journal of the Association for Information Science and Technology. 67(2016) no.10, S.2437-2453
  18. Kim, H.H.; Kim, Y.H.: Video summarization using event-related potential responses to shot boundaries in real-time video watching (2019) 0.00
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    Source
    Journal of the Association for Information Science and Technology. 70(2019) no.2, S.164-175
  19. Kim, H.H.; Kim, Y.H.: ERP/MMR algorithm for classifying topic-relevant and topic-irrelevant visual shots of documentary videos (2019) 0.00
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    Source
    Journal of the Association for Information Science and Technology. 70(2019) no.9, S.931-941
  20. Abdi, A.; Shamsuddin, S.M.; Aliguliyev, R.M.: QMOS: Query-based multi-documents opinion-oriented summarization (2018) 0.00
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    Source
    Information processing and management. 54(2018) no.2, S.318-338