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Endres-Niggemeyer, B.: Referierregeln und Referate : Abstracting als regelgesteuerter Textverarbeitungsprozeß (1985)
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Ruda, S.: Abstracting: eine Auswahlbibliographie (1992)
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Kuhlen, R.: In Richtung Summarizing für Diskurse in K3 (2006)
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Soricut, R.; Marcu, D.: Abstractive headline generation using WIDL-expressions (2007)
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Zajic, D.; Dorr, B.J.; Lin, J.; Schwartz, R.: Multi-candidate reduction : sentence compression as a tool for document summarization tasks (2007)
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Lee, J.-H.; Park, S.; Ahn, C.-M.; Kim, D.: Automatic generic document summarization based on non-negative matrix factorization (2009)
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Marcu, D.: Automatic abstracting and summarization (2009)
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Xiong, S.; Ji, D.: Query-focused multi-document summarization using hypergraph-based ranking (2016)
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Dammeyer, A.; Jürgensen, W.; Krüwel, C.; Poliak, E.; Ruttkowski, S.; Schäfer, Th.; Sirava, M.; Hermes, T.: Videoanalyse mit DiVA (1998)
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Haag, M.: Automatic text summarization : Evaluation des Copernic Summarizer und mögliche Einsatzfelder in der Fachinformation der DaimlerCrysler AG (2002)
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Lam, W.; Chan, K.; Radev, D.; Saggion, H.; Teufel, S.: Context-based generic cross-lingual retrieval of documents and automated summaries (2005)
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Nomoto, T.: Discriminative sentence compression with conditional random fields (2007)
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- Abstract
- The paper focuses on a particular approach to automatic sentence compression which makes use of a discriminative sequence classifier known as Conditional Random Fields (CRF). We devise several features for CRF that allow it to incorporate information on nonlinear relations among words. Along with that, we address the issue of data paucity by collecting data from RSS feeds available on the Internet, and turning them into training data for use with CRF, drawing on techniques from biology and information retrieval. We also discuss a recursive application of CRF on the syntactic structure of a sentence as a way of improving the readability of the compression it generates. Experiments found that our approach works reasonably well compared to the state-of-the-art system [Knight, K., & Marcu, D. (2002). Summarization beyond sentence extraction: A probabilistic approach to sentence compression. Artificial Intelligence 139, 91-107.].
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Shen, D.; Yang, Q.; Chen, Z.: Noise reduction through summarization for Web-page classification (2007)
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Wang, W.; Hwang, D.: Abstraction Assistant : an automatic text abstraction system (2010)
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Xu, D.; Cheng, G.; Qu, Y.: Preferences in Wikipedia abstracts : empirical findings and implications for automatic entity summarization (2014)
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Kuhlen, R.: Informationsaufbereitung III : Referieren (Abstracts - Abstracting - Grundlagen) (2004)
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- Grundlagen der praktischen Information und Dokumentation. 5., völlig neu gefaßte Ausgabe. 2 Bde. Hrsg. von R. Kuhlen, Th. Seeger u. D. Strauch. Begründet von Klaus Laisiepen, Ernst Lutterbeck, Karl-Heinrich Meyer-Uhlenried. Bd.1: Handbuch zur Einführung in die Informationswissenschaft und -praxis
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Endres-Niggemeyer, B.; Jauris-Heipke, S.; Pinsky, S.M.; Ulbricht, U.: Wissen gewinnen durch Wissen : Ontologiebasierte Informationsextraktion (2006)
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Endres-Niggemeyer, B.; Ziegert, C.: SummIt-BMT : (Summarize It in BMT) in Diagnose und Therapie, Abschlussbericht (2002)
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Meyer, R.: Allein, es wär' so schön gewesen : Der Copernic Summarzier kann Internettexte leider nicht befriedigend und sinnvoll zusammenfassen (2002)
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