-
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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- Source
- Journal of the American Society for Information Science and Technology. 56(2005) no.2, S.129-139
-
Sparck Jones, K.: Automatic summarising : the state of the art (2007)
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- Information processing and management. 43(2007) no.6, S.1449-1481
-
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.].
- Source
- Information processing and management. 43(2007) no.6, S.1571-1587
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Steinberger, J.; Poesio, M.; Kabadjov, M.A.; Jezek, K.: Two uses of anaphora resolution in summarization (2007)
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- Source
- Information processing and management. 43(2007) no.6, S.1663-1680
-
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.
- Source
- Information processing and management. 43(2007) no.6, S.1606-1618
-
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
- Source
- Journal of the American Society for Information Science and Technology. 57(2006) no.6, S.740-752
-
Dunlavy, D.M.; O'Leary, D.P.; Conroy, J.M.; Schlesinger, J.D.: QCS: A system for querying, clustering and summarizing documents (2007)
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- Abstract
- Information retrieval systems consist of many complicated components. Research and development of such systems is often hampered by the difficulty in evaluating how each particular component would behave across multiple systems. We present a novel integrated information retrieval system-the Query, Cluster, Summarize (QCS) system-which is portable, modular, and permits experimentation with different instantiations of each of the constituent text analysis components. Most importantly, the combination of the three types of methods in the QCS design improves retrievals by providing users more focused information organized by topic. We demonstrate the improved performance by a series of experiments using standard test sets from the Document Understanding Conferences (DUC) as measured by the best known automatic metric for summarization system evaluation, ROUGE. Although the DUC data and evaluations were originally designed to test multidocument summarization, we developed a framework to extend it to the task of evaluation for each of the three components: query, clustering, and summarization. Under this framework, we then demonstrate that the QCS system (end-to-end) achieves performance as good as or better than the best summarization engines. Given a query, QCS retrieves relevant documents, separates the retrieved documents into topic clusters, and creates a single summary for each cluster. In the current implementation, Latent Semantic Indexing is used for retrieval, generalized spherical k-means is used for the document clustering, and a method coupling sentence "trimming" and a hidden Markov model, followed by a pivoted QR decomposition, is used to create a single extract summary for each cluster. The user interface is designed to provide access to detailed information in a compact and useful format. Our system demonstrates the feasibility of assembling an effective IR system from existing software libraries, the usefulness of the modularity of the design, and the value of this particular combination of modules.
- Source
- Information processing and management. 43(2007) no.6, S.1588-1605
-
Mani, T.: Automatic summarization (2001)
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- Footnote
- Rez. in: JASIST 53(2002) no.5, S.410-411 (S.J. Lincicium)
- Pages
- 285 S
-
Craven, T.C.: Presentation of repeated phrases in a computer-assisted abstracting tool kit (2001)
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- Source
- Information processing and management. 37(2001) no.2, S.221-230
-
Endres-Niggemeyer, B.: SimSum : an empirically founded simulation of summarizing (2000)
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- Source
- Information processing and management. 36(2000) no.4, S.659-682
-
Pinto, M.: Engineering the production of meta-information : the abstracting concern (2003)
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- Source
- Journal of information science. 29(2003) no.5, S.405-418
-
Yusuff, A.: Automatisches Indexing and Abstracting : Grundlagen und Beispiele (2002)
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- Pages
- 53 S
-
Ye, S.; Chua, T.-S.; Kan, M.-Y.; Qiu, L.: Document concept lattice for text understanding and summarization (2007)
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- Abstract
- We argue that the quality of a summary can be evaluated based on how many concepts in the original document(s) that can be preserved after summarization. Here, a concept refers to an abstract or concrete entity or its action often expressed by diverse terms in text. Summary generation can thus be considered as an optimization problem of selecting a set of sentences with minimal answer loss. In this paper, we propose a document concept lattice that indexes the hierarchy of local topics tied to a set of frequent concepts and the corresponding sentences containing these topics. The local topics will specify the promising sub-spaces related to the selected concepts and sentences. Based on this lattice, the summary is an optimized selection of a set of distinct and salient local topics that lead to maximal coverage of concepts with the given number of sentences. Our summarizer based on the concept lattice has demonstrated competitive performance in Document Understanding Conference 2005 and 2006 evaluations as well as follow-on tests.
- Source
- Information processing and management. 43(2007) no.6, S.1643-1662
-
Harabagiu, S.; Hickl, A.; Lacatusu, F.: Satisfying information needs with multi-document summaries (2007)
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- Information processing and management. 43(2007) no.6, S.1619-1642
-
Liang, S.-F.; Devlin, S.; Tait, J.: Investigating sentence weighting components for automatic summarisation (2007)
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- Information processing and management. 43(2007) no.1, S.146-153
-
Moens, M.F.: Automatic indexing and abstracting of document texts (2000)
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- Pages
- 400 S
-
Hahn, U.: ¬Die Verdichtung textuellen Wissens zu Information : vom Wandel methodischer Paradigmen beim automatischen Abstracting (2004)
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- Pages
- S.51-68
-
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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- Source
- Information processing and management. 45(2009) no.1, S.20-34
-
Haag, M.: Automatic text summarization : Evaluation des Copernic Summarizer und mögliche Einsatzfelder in der Fachinformation der DaimlerCrysler AG (2002)
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- Footnote
- Diplomarbeit an der HBI Stuttgart. - Vgl. auch: nfd 53(2002) H.4, S.243-244
- Pages
- 211 S
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Chen, H.-H.; Kuo, J.-J.; Huang, S.-J.; Lin, C.-J.; Wung, H.-C.: ¬A summarization system for Chinese news from multiple sources (2003)
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- Source
- Journal of the American Society for Information Science and technology. 54(2003) no.13, S.1224-1236
