Search (15 results, page 1 of 1)

  • × theme_ss:"Automatisches Abstracting"
  1. Kuhlen, R.: Abstracts, abstracting : intellektuelle und maschinelle Verfahren (1990) 0.01
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    Year
    1990
  2. Endres-Niggemeyer, B.: Kognitive Modellierung des Abstracting (1991) 0.01
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    Source
    Deutscher Dokumentartag 1990. 1. Deutsch-deutscher Dokumentartag, 25.-27.9.90, Fulda. Proceedings. Hrsg. W. Neubauer u. U. Schneider-Briehn
  3. Wang, W.; Hwang, D.: Abstraction Assistant : an automatic text abstraction system (2010) 0.01
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    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.9, S.1790-1799
    Year
    2010
  4. Paice, C.D.: Automatic abstracting (1994) 0.01
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    Abstract
    The final report of the 2nd British Library abstracting project (the BLAB project), 1990-1992, which was carried out partly at the Computing Department of Lancaster University, and partly at the Centre for Computational Linguistics, UMIST. This project built on the results of the first project, of 1985-1987, to build a system designed create abstracts automatically from given texts
  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
    Year
    2010
  6. 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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    Abstract
    Keyphrases represent the main topics a text is about. In this article, we introduce SemGraph, an unsupervised algorithm for extracting keyphrases from a collection of texts based on a semantic relationship graph. The main novelty of this algorithm is its ability to identify semantic relationships between words whose presence is statistically significant. Our method constructs a co-occurrence graph in which words appearing in the same document are linked, provided their presence in the collection is statistically significant with respect to a null model. Furthermore, the graph obtained is enriched with information from WordNet. We have used the most recent and standardized benchmark to evaluate the system ability to detect the keyphrases that are part of the text. The result is a method that achieves an improvement of 5.3% and 7.28% in F measure over the two labeled sets of keyphrases used in the evaluation of SemEval-2010.
  7. Dorr, B.J.; Gaasterland, T.: Exploiting aspectual features and connecting words for summarization-inspired temporal-relation extraction (2007) 0.00
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    Abstract
    This paper presents a model that incorporates contemporary theories of tense and aspect and develops a new framework for extracting temporal relations between two sentence-internal events, given their tense, aspect, and a temporal connecting word relating the two events. A linguistic constraint on event combination has been implemented to detect incorrect parser analyses and potentially apply syntactic reanalysis or semantic reinterpretation - in preparation for subsequent processing for multi-document summarization. An important contribution of this work is the extension of two different existing theoretical frameworks - Hornstein's 1990 theory of tense analysis and Allen's 1984 theory on event ordering - and the combination of both into a unified system for representing and constraining combinations of different event types (points, closed intervals, and open-ended intervals). We show that our theoretical results have been verified in a large-scale corpus analysis. The framework is designed to inform a temporally motivated sentence-ordering module in an implemented multi-document summarization system.
  8. Goh, A.; Hui, S.C.: TES: a text extraction system (1996) 0.00
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    Date
    26. 2.1997 10:22:43
  9. Robin, J.; McKeown, K.: Empirically designing and evaluating a new revision-based model for summary generation (1996) 0.00
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    Date
    6. 3.1997 16:22:15
  10. Jones, P.A.; Bradbeer, P.V.G.: Discovery of optimal weights in a concept selection system (1996) 0.00
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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
  11. Vanderwende, L.; Suzuki, H.; Brockett, J.M.; Nenkova, A.: Beyond SumBasic : task-focused summarization with sentence simplification and lexical expansion (2007) 0.00
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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.
  12. Wu, Y.-f.B.; Li, Q.; Bot, R.S.; Chen, X.: Finding nuggets in documents : a machine learning approach (2006) 0.00
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    Date
    22. 7.2006 17:25:48
  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
  14. Oh, H.; Nam, S.; Zhu, Y.: Structured abstract summarization of scientific articles : summarization using full-text section information (2023) 0.00
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    Date
    22. 1.2023 18:57:12
  15. Jiang, Y.; Meng, R.; Huang, Y.; Lu, W.; Liu, J.: Generating keyphrases for readers : a controllable keyphrase generation framework (2023) 0.00
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    Date
    22. 6.2023 14:55:20