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Vanderwende, L.; Suzuki, H.; Brockett, J.M.; Nenkova, A.: Beyond SumBasic : task-focused summarization with sentence simplification and lexical expansion (2007)
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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.
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Yusuff, A.: Automatisches Indexing and Abstracting : Grundlagen und Beispiele (2002)
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- Imprint
- Potsdam : Fachhochschule, FB A-B-D
- Language
- d
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Wu, Y.-f.B.; Li, Q.; Bot, R.S.; Chen, X.: Finding nuggets in documents : a machine learning approach (2006)
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- Date
- 22. 7.2006 17:25:48
-
Haag, M.: Automatic text summarization (2002)
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Hahn, U.: ¬Die Verdichtung textuellen Wissens zu Information : vom Wandel methodischer Paradigmen beim automatischen Abstracting (2004)
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Deutsche Patentdatenbank mit maschinellen Abstract-Übersetzungen (2005)
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Over, P.; Dang, H.; Harman, D.: DUC in context (2007)
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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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-
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.].
-
Shen, D.; Yang, Q.; Chen, Z.: Noise reduction through summarization for Web-page classification (2007)
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Kuhlen, R.: Informationsaufbereitung III : Referieren (Abstracts - Abstracting - Grundlagen) (2004)
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- Source
- 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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