M. J. Witbrock and V. O. Mittal, "Ultra-summarization: A statistical approach to generating highly condensed nonextractive summaries," in Proc. ACM SIGIR '99, 1999, pp. 315--316. Home/Search Document Details and Download
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Model Selection in Summary Evaluation - Perez-Breva, Yoshimi (2002) (Correct)
....of the text, 2. Feature selection, that is selection of units of content, 3. Compilation of the selected units of content. The inference step characterizes each document in terms of a set of latent variables that may account for syntax, lexical cohesion [7] segmentation [9] and or statistics [21] of the document. The goal of the second step is to choose the subset of units of content identified in the inference step, that best describes the entire document up to a certain predetermined precision. The last step (compilation) recombines the units of content of the document into a ....
Michael J. Witbrock and Vibhu O. Mittal. Ultra-summarization: A statistical approach to generating highly condensed non-extractive summaries (poster abstract). In Research and Development in Information Retrieval, pages 315 316, 1999.
Summarization beyond sentence extraction: A probabilistic.. - Knight, Marcu (2002) (1 citation) (Correct)
....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 probabilistic model trained directly on #Summary, Document# pairs. However, the Summary here restricted to headlines, and these models have yet to scale up to generating multiple sentence abstracts ....
M. Witbrock, V. Mittal, Ultra-summarization: A statistical approach to generating highly condensed nonextractive summaries, in: Proceedings of the 22nd International Conference on Research and Development in Information Retrieval (SIGIR-99), Poster Session, Berkeley, CA, 1999, pp. 315--316.
Statistics-Based Summarization - Step One: Sentence Compression - Knight, Marcu (2000) (12 citations) (Correct)
.... a number of researchers have started to address the problem of generating coherent summaries: McKeown et al. 1999) Barzilay et al. 1999) and Jing and McKeown (1999) in the context of multidocument summarization; Mani et al. 1999) in the context of revising single document extracts; and Witbrock and Mittal (1999) in the context of headline generation. The approach proposed by Witbrock and Mittal (1999) is the only one that applies a probabilistic model trained directly on hHeadline, Documenti pairs. However, this model has yet to scale up to generating multiple sentence abstracts as well as well formed, ....
....McKeown et al. 1999) Barzilay et al. 1999) and Jing and McKeown (1999) in the context of multidocument summarization; Mani et al. 1999) in the context of revising single document extracts; and Witbrock and Mittal (1999) in the context of headline generation. The approach proposed by Witbrock and Mittal (1999) is the only one that applies a probabilistic model trained directly on hHeadline, Documenti pairs. However, this model has yet to scale up to generating multiple sentence abstracts as well as well formed, Copyright c fl 2000, American Association for Artificial Intelligence (www.aaai.org) All ....
Witbrock, M., and Mittal, V. 1999. Ultrasummarization: A statistical approach to generating highly condensed non-extractive summaries. In Proceedings of the 22nd International Conference on Research and Development in Information Retrieval (SIGIR '99), Poster Session, 315--316.
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M. J. Witbrock and V. O. Mittal, "Ultra-summarization: A statistical approach to generating highly condensed nonextractive summaries," in Proc. ACM SIGIR '99, 1999, pp. 315--316.
Automatic Title Generation using EM - Paul Kennedy Mit (Correct)
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Witbrock, M.J. and Mittal, V.O, Ultra-Summarization: A Statistical Approach to Generating Highly Condensed Non-Extractive Summaries, in Proc. SIGIR 99 Research and Development in Information Retrieval (Berkeley, August 15-19, 1999), ACM Press, pp. 315-316.
Automatic Title Generation for Spoken Broadcast News - Rong Jin Language (Correct)
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Michael Witbrock and Vibhu Mittal. Ultra-Summarization: A Statistical Approach to Generating Highly Condensed Non-Extractive Summaries. Proceedings of SIGIR 99, Berkeley, CA, August 1999.
ICSLP 2000 - 6th International Conference of Spoken.. - Beijing China Title (2000) (Correct)
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Michael Witbrock and Vibhu Mittal, "UltraSummarization: A Statistical Approach to Generating Highly Condensed Non-Extractive Summaries", Proceedings of SIGIR 99, Berkeley, CA, August 1999
Language Models for Hierarchical Summarization - Lawrie (2003) (1 citation) (Correct)
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Witbrock, M., and Mittal, V. Ultra-summarization: A statistical approach to generating highly condensed non-extractive summaries. In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval (1999), pp. 315--316.
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