Number of found records: 80
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ENDRES-NIGGEMEYER, Brigitte; NEUGEBAUER, Elisabeth |
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Professional summarising: no cognitive simulation without observation. |
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Journal of the American Society for Information Science, 1998, vol.49, n.6, pp.486-506 |
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On line (11/05/2005) |
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Professional summarising includes the cognitive process of abstracting, indexing and classifying as performed by the expert summarisers who prepare records for bibliographic information systems. In order to simulate their skilled performance, a "grounded" (or "naturalistic") cognitive model of expert summarisation has been developed, using 54 thinking-aloud protocols of 6 expert abstractors. The model comprises a toolbox of empirically founded strategies, principles of sophisticated process organisation and interpreted working steps where the interaction of cognitive strategies can be investigated. In the computerised simulation, cognitive strategies are represented by object-oriented agents grouped around dedicated blackboards. While the main scientific goal of the system is to check and improve the cognitive model, the system also serves practical presentation purposes. It will be distributed on CD-ROM as part of a textbook and thus help to understand the complicated cognitive task of summarising. (AU) |
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Summarisation; Cognitive modelling; Expert system |
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GOLDSTEIN, Jade; MITTAL, Vibhu O.; CARBONELL, Jamie; KANTROWITZ, Mark |
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Multi-Document Summarization by Sentence Extraction. |
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In HAHN, Udo, LIN, Chin-Yew, MANI, Inderjeet, RADEV, Dragomir R., (Eds), Proceedings of the Workshop on Automatic Summarization at the 6th Applied Natural Language Processing Conference and the 1st Conference of the North American Chapter of the Association for Computational Linguistics, Seattle, WA, April 2000. |
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This paper discusses a text extraction approach to multidocument summarization that builds on single-document summarization methods by using additional, available in-, formation about the document set as a whole and the relationships between the documents. Multi-document summarization differs from single in that the issues of compression, speed, redundancy and passage selection are critical in the formation of useful summaries. Our approach addresses these issues by using domain independent techniques based mainly on fast, statistical processing, a metric for reducing redundancy and maximizing diversity in the selected passages, and a modular framework to allow easy parameterization for different genres, corpora characteristics and user requirements. (AU) |
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multidocument summarization; TIPSTER |
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GREWAL, Amardeep,; ALLISON, Timothy; DIMITROV, Stanko; RADEV, Dragomir |
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Multi-document summarization using o_ the shelf compression software. |
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In RADEV, Dragomir; TEUFEL, Simone, editors, HLT-NAACL 2003 Workshop: Text Summarization (DUC03), pages 17{24, Edmonton, Alberta, Canada, May 31 - June 1 2003. Association for Computational Linguistics. |
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This study examines the usefulness of common off the shelf compression software such as gzip in enhancing already existing summaries and producing summaries from scratch. Since the gzip algorithm works by removing repetitive data from a file in order to compress it, we should be able to determine which sentences in a summary contain the least repetitive data by judging the gzipped size of the summary with the sentence compared to the gzipped size of the summary without the sentence. By picking the sentence that increased the size of the summary the most, we hypothesized that the summary will gain the sentence with the most new information. This hypothesis was found to be true in many cases and to varying degrees in this study. (AU) |
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summarization; evaluation; gzip |
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HOVY, Eduard; LIN, Chin-Yew |
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Automated text summarization in SUMMARIST |
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Advances in Automatic Text Summarization, I. Mani and M. Maybury (editors), 1999 |
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SUMMARIST is an attempt to create a robust automated text summarization system, based on the 'equation': summarization = topic identification + interpretation + generation. Each of these stages contains several independent modules, many of them trained on large corpora of text. We describe the system's architecture and provide details of some of its modules. (AU) |
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SUMMARIST; automated text summarization system |
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