Search (13 results, page 1 of 1)

  • × theme_ss:"Automatisches Abstracting"
  • × year_i:[2010 TO 2020}
  1. Hahn, U.: Automatisches Abstracting (2013) 0.01 
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    Date
    5. 4.2013 13:44:59
    Source
    Grundlagen der praktischen Information und Dokumentation. Handbuch zur Einführung in die Informationswissenschaft und -praxis. 6., völlig neu gefaßte Ausgabe. Hrsg. von R. Kuhlen, W. Semar u. D. Strauch. Begründet von Klaus Laisiepen, Ernst Lutterbeck, Karl-Heinrich Meyer-Uhlenried
  2. Galgani, F.; Compton, P.; Hoffmann, A.: Summarization based on bi-directional citation analysis (2015) 0.00 
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    Abstract
    Automatic document summarization using citations is based on summarizing what others explicitly say about the document, by extracting a summary from text around the citations (citances). While this technique works quite well for summarizing the impact of scientific articles, other genres of documents as well as other types of summaries require different approaches. In this paper, we introduce a new family of methods that we developed for legal documents summarization to generate catchphrases for legal cases (where catchphrases are a form of legal summary). Our methods use both incoming and outgoing citations, and we show how citances can be combined with other elements of cited and citing documents, including the full text of the target document, and catchphrases of cited and citing cases. On a legal summarization corpus, our methods outperform competitive baselines. The combination of full text sentences and catchphrases from cited and citing cases is particularly successful. We also apply and evaluate the methods on scientific paper summarization, where they perform at the level of state-of-the-art techniques. Our family of citation-based summarization methods is powerful and flexible enough to target successfully a range of different domains and summarization tasks.
    Date
    5. 9.2014 10:18:18
  3. Wang, S.; Koopman, R.: Embed first, then predict (2019) 0.00 
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    Content
    DOI:10.5771/0943-7444-2019-5-364.
    Source
    Knowledge organization. 46(2019) no.5, S.364-370
  4. Plaza, L.; Stevenson, M.; Díaz, A.: Resolving ambiguity in biomedical text to improve summarization (2012) 0.00 
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    Date
    5. 9.2014 10:18:18
  5. Xiong, S.; Ji, D.: Query-focused multi-document summarization using hypergraph-based ranking (2016) 0.00 
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    Date
    5. 9.2014 10:18:18
  6. 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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    Date
    5. 9.2014 10:18:18
  7. 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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    Date
    5. 9.2014 10:18:18
  8. 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
  9. Sankarasubramaniam, Y.; Ramanathan, K.; Ghosh, S.: Text summarization using Wikipedia (2014) 0.00 
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    Date
    5. 9.2014 10:18:18
  10. 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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    Date
    5. 8.2019 19:07:14
  11. 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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    Date
    5. 9.2014 10:18:18
  12. Abdi, A.; Shamsuddin, S.M.; Aliguliyev, R.M.: QMOS: Query-based multi-documents opinion-oriented summarization (2018) 0.00 
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    Date
    5. 9.2014 10:18:18
  13. Kim, H.H.; Kim, Y.H.: Generic speech summarization of transcribed lecture videos : using tags and their semantic relations (2016) 0.00 
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    Date
    22. 1.2016 12:29:41

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