Number of found records: 80
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ANGHELUTA, Roxana; DE BUSSER, Rik; MOENS, Marie-Francine |
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The use of topic Segmentation for Automatic Summarization. |
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DUC 2002 |
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PDF |
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Topic segmentation can be used as a preprocessing step in numerous natural language processing applications. In this short paper, we will discuss how we adapted our segmentation algorithm for automatic summarization. (AU) |
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topic segmentation; natural language processing; automatic summarization |
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ENDRES-NIGGEMEYER, Brigitte |
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SimSum: an empirically founded simulation of summarizing |
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Information Processing and Management, 2000, vol. 36,n.4, pp.659-682 |
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On line (11/05/2005) |
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SimSum (Simulation of Summarizing) simulates 20 real-world working steps of expert summarizers. It presents an empirically founded cognitive model of summarizing and demonstrates that human summarization strategies can be simulated. The cognitive model operationalizes the discourse processing model developed by Kintsch and van Dijk (1983). Knowledge engineering followed the KADS approach, empirical modeling used methods of grounded theory development. The observed strategies of expert summarizers have given rise to cooperating object-oriented agents communicating through dedicated blackboards. Each agent is implemented as a CLOS object with an assigned actor at the multimedia user interface. The interface is realized with Macromedia Director. Communication between CLOS and Macromedia Director is mediated by Apple Events. According to first evaluation results in an educational environment, SimSum transmits summarization know-how effectively. It is, however, not designed as a tutorial system and serves active and curious users best. We are starting its expansion to summarizing in the WWW. (AU) |
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Automatic text analysis; Automatic abstracting; Models; Cognitive aspects ; SimSum. (LISA) |
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Harabagiu, Sanda; Hickl, Andrew; Lacatusu, Finley |
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Satisfying information needs with multi-document summaries |
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Information Processing & Management, Nov. 2007, Vol. 43 Issue 6, p1619-1642 |
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On line (04/2008) (Only UGR) |
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Generating summaries that meet the information needs of a user relies on (1) several forms of question decomposition; (2) different summarization approaches; and (3) textual inference for combining the summarization strategies. This novel framework for summarization has the advantage of producing highly responsive summaries, as indicated by the evaluation results (DB) |
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Automatic abstracts; summaries |
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Hirao, Tsutomu; Okumura, Manabu; Yasuda, Norihito; Isozaki, Hideki |
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Supervised automatic evaluation for summarization with voted regression model |
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Information Processing & Management, Nov2007, Vol. 43 Issue 6, p1521-1535 |
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On line (04/2008) (Only UGR) |
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The high quality evaluation of generated summaries is needed if we are to improve automatic summarization systems. Although human evaluation provides better results than automatic evaluation methods, its cost is huge and it is difficult to reproduce the results. Therefore, we need an automatic method that simulates human evaluation if we are to improve our summarization system efficiently. Although automatic evaluation methods have been proposed, they are unreliable when used for individual summaries. To solve this problem, we propose a supervised automatic evaluation method based on a new regression model called the voted regression model (VRM). VRM has two characteristics: (1) model selection based on 'corrected AIC' to avoid multicollinearity, (2) voting by the selected models to alleviate the problem of overfitting. Evaluation results obtained for TSC3 and DUC2004 show that our method achieved error reductions of about 17-51% compared with conventional automatic evaluation methods. Moreover, our method obtained the highest correlation coefficients in several different experiments (DB) |
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Automatic abstract; automatic evaluation; VRM |
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