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Hahn, U.: Automatisches Abstracting (2013)
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- 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
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Kuhlen, R.: Abstracts, abstracting : intellektuelle und maschinelle Verfahren (1990)
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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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Kuhlen, R.: Abstracts, abstracting : intellektuelle und maschinelle Verfahren (1997)
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Endres-Niggemeyer, B.: Kognitive Modellierung des Abstracting (1991)
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Saggion, H.; Lapalme, G.: Selective analysis for the automatic generation of summaries (2000)
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Hirao, T.; Okumura, M.; Yasuda, N.; Isozaki, H.: Supervised automatic evaluation for summarization with voted regression model (2007)
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Hahn, U.: ¬Die Verdichtung textuellen Wissens zu Information : vom Wandel methodischer Paradigmen beim automatischen Abstracting (2004)
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Reeve, L.H.; Han, H.; Brooks, A.D.: ¬The use of domain-specific concepts in biomedical text summarization (2007)
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Endres-Niggemeyer, B.: Referierregeln und Referate : Abstracting als regelgesteuerter Textverarbeitungsprozeß (1985)
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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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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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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
