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Uyttendaele, C.; Moens, M.-F.; Dumortier, J.: SALOMON: automatic abstracting of legal cases for effective access to court decisions (1998)
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Moens, M.-F.: Summarizing court decisions (2007)
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Moens, M.-F.; Uyttendaele, C.: Automatic text structuring and categorization as a first step in summarizing legal cases (1997)
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Moens, M.-F.; Uyttendaele, C.; Dumotier, J.: Abstracting of legal cases : the potential of clustering based on the selection of representative objects (1999)
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-
Johnson, F.: Automatic abstracting research (1995)
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Harabagiu, S.; Hickl, A.; Lacatusu, F.: Satisfying information needs with multi-document summaries (2007)
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-
Goh, A.; Hui, S.C.: TES: a text extraction system (1996)
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- Date
- 26. 2.1997 10:22:43
-
Robin, J.; McKeown, K.: Empirically designing and evaluating a new revision-based model for summary generation (1996)
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- Date
- 6. 3.1997 16:22:15
-
Jones, P.A.; Bradbeer, P.V.G.: Discovery of optimal weights in a concept selection system (1996)
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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
-
Liang, S.-F.; Devlin, S.; Tait, J.: Investigating sentence weighting components for automatic summarisation (2007)
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-
Martinez-Romo, J.; Araujo, L.; Fernandez, A.D.: SemGraph : extracting keyphrases following a novel semantic graph-based approach (2016)
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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.
-
Kuhlen, R.: Abstracts, abstracting : intellektuelle und maschinelle Verfahren (1990)
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- Source
- Grundlagen der praktischen Information und Dokumentation. 3. Aufl. Hrsg.: M. Buder u.a. Bd.1
-
Pinto, M.: Engineering the production of meta-information : the abstracting concern (2003)
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-
Mani, T.: Automatic summarization (2001)
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- Type
- m
-
Yeh, J.-Y.; Ke, H.-R.; Yang, W.-P.; Meng, I.-H.: Text summarization using a trainable summarizer and latent semantic analysis (2005)
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- Abstract
- This paper proposes two approaches to address text summarization: modified corpus-based approach (MCBA) and LSA-based T.R.M. approach (LSA + T.R.M.). The first is a trainable summarizer, which takes into account several features, including position, positive keyword, negative keyword, centrality, and the resemblance to the title, to generate summaries. Two new ideas are exploited: (1) sentence positions are ranked to emphasize the significances of different sentence positions, and (2) the score function is trained by the genetic algorithm (GA) to obtain a suitable combination of feature weights. The second uses latent semantic analysis (LSA) to derive the semantic matrix of a document or a corpus and uses semantic sentence representation to construct a semantic text relationship map. We evaluate LSA + T.R.M. both with single documents and at the corpus level to investigate the competence of LSA in text summarization. The two novel approaches were measured at several compression rates on a data corpus composed of 100 political articles. When the compression rate was 30%, an average f-measure of 49% for MCBA, 52% for MCBA + GA, 44% and 40% for LSA + T.R.M. in single-document and corpus level were achieved respectively.
-
Sweeney, S.; Crestani, F.; Losada, D.E.: 'Show me more' : incremental length summarisation using novelty detection (2008)
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Wei, F.; Li, W.; Lu, Q.; He, Y.: Applying two-level reinforcement ranking in query-oriented multidocument summarization (2009)
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Galgani, F.; Compton, P.; Hoffmann, A.: Summarization based on bi-directional citation analysis (2015)
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Kuhlen, R.: Abstracts, abstracting : intellektuelle und maschinelle Verfahren (1997)
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- Source
- Grundlagen der praktischen Information und Dokumentation: ein Handbuch zur Einführung in die fachliche Informationsarbeit. 4. Aufl. Hrsg.: M. Buder u.a
-
Haag, M.: Automatic text summarization (2002)
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