Number of found records: 80
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ANDO, Rie; BOGURAEV, Branimir K.; BYRD, Roy; [et al]. |
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Multi-Document Summarization by Visualizing Topical Content. |
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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 describes a framework for multidocument summarization which combines three premises: coherent themes can be identified reliably; highly representative themes, running across subsets of the document collection, can function as multi-document summary surrogates; and effective end-use of such themes should be facilitated by a visualization environment which clarifies the relationship between themes and documents. We present algorithms that formalize our framework, describe an implementation, and demonstrate a prototype system and interface. (AU) |
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automatic summarization; topical content; visualization |
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BARZILAY, Regina; ELHADAD, Michael |
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Using Lexical Chains for Text Summarization |
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MANI, Inderjeet, MAYBURY, Mark T. (Eds), Advances in Automatic Text Summarization, pp.111-121. The MIT Press: 1999. |
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We investigate one technique to produce a summary of an original text without requiring its full semantic interpretation, but instead relying on a model of the topic progression in the text derived from lexical chains. We present a new algorithm to compute lexical chains in a text, merging several robust knowledge sources: the WordNet thesaurus, a part-of-speech tagger, shallow parser for the identification of nominal groups, and a segmentation algorithm. Summarization proceeds in four steps: the original text is segmented, lexical chains are constructed, strong chains are identified and significant sentences are extracted. We present in this paper empirical results on the identification of strong chains and of significant sentences. Preliminary results indicate that quality indicative summaries are produced. Pending problems are identified. Plans to address these short-comings are briefly presented. (AU) |
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summarization; lexical chains; extracting |
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BARZILAY, Regina; MCKEOWN, Kathleen R.; ELHADAD, Michael |
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Inferring Strategies for Sentence Ordering in Multidocument News Summarization. |
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In Journal of Artificial Intelligence Research, 2002, pp.35-55. |
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The problem of organizing information for multidocument summarization so that the generated summary is coherent has received relatively little attention. While sentence ordering for single document summarization can be determined from the ordering of sentences in the input article, this is not the case for multidocument summarization where summary sentences may be drawn from different input articles. In this paper, we propose a methodology for studying the properties of ordering information in the news genre and describe experiments done on a corpus of multiple acceptable orderings we developed for the task. Based on these experiments, we implemented a strategy for ordering information that combines constraints from chronological order of events and topical relatedness. Evaluation of our augmented algorithm shows a significant improvement of the ordering over two baseline strategies. (AU) |
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summarization; algorithm; multidocument |
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BUYUKKOKTEN, Orkut; GARCÍA-MOLINA, Héctor; PAEPCKE, Andreas |
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Seeing the Whole in Parts: Text Summarization for Web Browsing on Handheld Devices |
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Digital Library Project (InfoLab), Stanford University, The 10th International WWW Conference Hong Kong, China - May 1-5, 2001 |
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On line ( 15/06/2004) |
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We introduce five methods for summarizing parts of Web pages on handheld devices, such as personal digital assistants (PDAs), or cellular phones. Each Web page is broken into text units that can each be hidden, partially displayed, made fully visible, or summarized. The methods accomplish summarization by different means. One method extracts significant keywords from the text units, another attempts to find each text unit's most significant sentence to act as a summary for the unit. We use information retrieval techniques, which we adapt to the World-Wide Web context. We tested the relative performance of our five methods by asking human subjects to accomplish single-page information search tasks using each method. We found that the combination of keywords and single-sentence summaries provides significant improvements in access times and number of pen actions, as compared to other schemes. (AU) |
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Personal Digital Assistant; PDA; Handheld Computers; Mobile Computing; Summarization; WAP; Wireless Computing; Ubiquitous Computing |
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