Regina Barzilay, Noemie Elhadad, and Kathleen R. McKeown. 2001. Sentence ordering in multidocument summarization. In Proceedings of the 1st Human Language Technology Conference, San Diego, California.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....usually obtain documents in several different points of view, namely sub topics, even if the query is about one topic. We think that, in such a case, it would be preferable to show the outline of sub topics explicitly and present sub summaries for sub topics along with the outline. Barzilay et al.[1] reported the following findings by the experience in which subjects arrange the same set of extracted important sentences so as to maximize readability of text. # Firstly, the total order of sentences depends on subjects, and there are several possibilities of order of sentences. # The order, ....

R. Barzilay, N. Elhadad, and K. R. MaKeown. Sentence ordering in multidocument summarization. In Proceedings of the the first International Conference on Human Language Technology Research (HLT 2001.

....All rights reserved. PII: S0004 3702(02)00222 9 catenation of textual segments does not yield coherent outputs. Recently, a number of researchers have started to address the problem of generating coherent summaries: McKeown et al. 26] Barzilay et al. 3] Jing and McKeown [15] Barzilay et al. [2], and Marcu and Gerber [25] in the context of multidocument summarization; and Mani et al. 22] in the context of revising single document extracts. The approaches proposed by Witbrock and Mittal [29] Banko et al. 1] Berger and Mittal [5] Jing and Hauptmann [16] are the only ones that apply a ....

R. Barzilay, N. Elhadad, K. McKeown, Sentence ordering in multidocument summarization, in: Proceedings of the First International Conference on Human Language Technology Research (HLT-01), San Diego, CA, 2001, pp. 149--156.

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Regina Barzilay, Noemie Elhadad, and Kathleen R. McKeown. 2001. Sentence ordering in multidocument summarization. In Proceedings of the 1st Human Language Technology Conference, San Diego, California.

.... machine learning and statistical techniques to identify similar sentences (set set of similar sentences is called a themes) across the input articles [5, 6] It then uses an alignment of parse trees to find the intersection of similar phrases within sentences [2] It orders the selected themes [1] and uses language generation to cut and paste together similar phrases from the theme sentences. Each theme corresponds to roughly one sentence of the summary. For biographical documents, it uses an alternate system, DEMS (Dissimilarity Engine for Multidocument Summarization) 13] tuned to the ....

R. Barzilay, N. Elhadad, and K. R. McKeown. Sentence ordering in multidocument summarization. In Proceedings of the 1st Human Language Technology Conference, San Diego, California, 2001.

....long time period. Consequently, we developed an alternate summarization strategy that can be adapted to documents of different types, including biographies and multiple weakly related events. To summarize documents on the same event, the Columbia summarizer uses an enhanced ver sion of MultiGen [Barzilay et al. 1999; Hatzivas siloglou et al. 1999; McKeown et al. 1999; Barzi lay et al. 2001; Hatzivassiloglou et al. 2001] for biographical documents, it uses an alternate sys tem, DEMS (Dissimilarity Engine for Multidocument Summarization) tuned to the biographical task; and for sets of loosely similar documents, it uses DEMS with a more general configuration. DEMS incorporates ....

Regina Barzilay, Noemie Elbadad, and Kathleen R. McKeown. Sentence Ordering in Multidocument Summarization. In Proceedings of the 1st Human Language Technology Conference, San Diego, California, 2001.

....that can be adapted to documents of different types, including biographies and multiple weakly related events. To summarize documents on the same event, the Columbia summarizer uses an enhanced version of MultiGen [ Barzilay et al. 1999; Hatzivassiloglou et al. 1999; McKeown et al. 1999; Barzilay et al. 2001; Hatzivassiloglou et al. 2001 ] for biographical documents, it uses an alternate system, DEMS (Dissimilarity Engine for Multidocument Summarization) tuned to the biographical task; and for sets of loosely similar documents, it uses DEMS with a more general con guration. DEMS incorporates ....

Regina Barzilay, Noemie Elhadad, and Kathleen R. McKeown. Sentence Ordering in Multidocument Summarization. In Proceedings of the 1st Human Language Technology Conference, San Diego, California, 2001.

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Regina Barzilay, Noemie Elhadad, and Kathleen R. McKeown. Sentence ordering in multi-document summarization. In Proceedings of HLT, San Diego, CA, 2001.

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