Dragomir R. Radev. Generating Natural Language Summaries from Multiple On-Line Sources: Language Reuse and Regeneration . Unpublished PhD thesis, Columbia University, 1999. Home/Search Document Details and Download
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Detecting Discrepancies in Numeric Estimates Using.. - White, Cardie, Ng (2002) (Correct)
....a significantly more complete picture of the available information than the latest article. 1. INTRODUCTION Previous work in multidocument summarization has pointed to the importance of identifying differences and discrepancies in the information that is reported across multiple news sources [9, 12]. To our knowledge, however, this problem has not yet been systematically or thoroughly investigated. Radev and McKeown [9] for example, identify discrepancy detection as a potential problem for multidocument summarizers via anecdotal evidence, but provide no empirical evidence to indicate how ....
....in multidocument summarization has pointed to the importance of identifying differences and discrepancies in the information that is reported across multiple news sources [9, 12] To our knowledge, however, this problem has not yet been systematically or thoroughly investigated. Radev and McKeown [9], for example, identify discrepancy detection as a potential problem for multidocument summarizers via anecdotal evidence, but provide no empirical evidence to indicate how often such differences actually represent significant discrepancies in the available information, vs. simple updates in what ....
D. R. Radev and K. R. McKeown. Generating natural language summaries from multiple on-line sources. Computational Linguistics, 24(3):469--500, 1988.
Creating and Evaluating Multi-Document Sentence.. - Goldstein, Mittal.. (2000) (5 citations) (Correct)
....applied statistical techniques (frequency analysis, variance analysis, etc. to linguistic units suchastokens, names, anaphora, etc. e.g. 27, 19, 9, 18, 2] Other approaches include the utility of discourse structure [14] the combination of information extraction and language generation [11, 17, 24, 21, 16], and using machine learning to find patterns in text [28, 4, 26] Several researchers have extended various aspects of the single document approaches to look at multi documentsummarization [13, 21, 3, 7, 15] These include comparing templates filled in by extracting information using ....
.... structure [14] the combination of information extraction and language generation [11, 17, 24, 21, 16] and using machine learning to find patterns in text [28, 4, 26] Several researchers have extended various aspects of the single document approaches to look at multi documentsummarization [13, 21, 3, 7, 15]. These include comparing templates filled in by extracting information using specialized, domain specific knowledge sources from the document, and then generating natural language summaries from the templates [21] comparing named entities extracted using specialized lists between ....
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Dragomir R. Radev and Kathleen R. McKeown. Generating natural language summaries from multiple online sources. Compuutational Linguistics, 24(3), 1998.
Towards an ANN-based Approach to Automatic Sentence.. - Mu, Weijun, Tianshun (Correct)
....Abstracting is receiving more and more attention of NLP researchers along with the IE(Information Extraction) IR(Information Retrieval) and IF (Information Filtering) technique recently. Many automatic abstracting systems have been proposed. For example, SUMMONS [McKeown et al., 1995; Radev et al., 1998], SUMMARIST [Hovy et al., 1997; Lin, 1998] COSYMATS [Aretoulaki, 1997] SUMMAC [Sanderson, 1998] SJTUCAA [Wang et al., 1996] FDASCT [Wu et al., 1996] and so on. Tombros(1997) presented a general automatic text abstracting model which generates the abstract of the text in two steps: the source ....
Radev, Dragomir R., Kathleen R. McKeown, 1998 Generating Natural Language Summaries from Multiple On-Line Sources, Computational Linguistics, Vol.24, No.3, pp. 469-500.
Towards Multidocument Summarization by.. - McKeown, Klavens, .. (1999) (12 citations) (Correct)
....Hovy 1997) discourse structure (Marcu 1997; Marcu 1998) and user features from the query (Strzalkowski et al. 1998) to find key sentences. While most of the work to date focuses on summarization of single articles, early work is beginning to emerge on summarization across multiple documents. (Radev and McKeown 1998) use a symbolic approach to summarization, pairing information extraction systems with language generation. The result is a domain dependent system for summarization of multiple news articles on the same event, highlighting how perspective of the event has changed over time. In ongoing work at ....
Dragomir R. Radev and Kathleen R. McKeown. Generating Natural Language Summaries from Multiple On-Line Sources. Computational Linguistics, 24(3):469--500, September 1998.
Summarizing Text Documents: Sentence Selection and .. - Goldstein.. (1999) (17 citations) (Correct)
....problem through the seventies and eighties (e.g. 17, 25] The resources devoted to addressing this problem grew by several orders of magnitude with the advent of the world wide web and large scale search engines. Several innovative approaches began to be explored: linguistic approaches (e.g. [1, 2, 4, 12,14,15, 18]) statistical and information centric approaches (e.g. 6, 9, 16, 24] and combinations of the two (e.g. 3, 24,26] The TIPSTER Phase III Program, an information retrieval initiative of the US Defense Department funded several of these projects on summarization [27] Almost all of this work ....
.... approaches (e.g. 6, 9, 16, 24] and combinations of the two (e.g. 3, 24,26] The TIPSTER Phase III Program, an information retrieval initiative of the US Defense Department funded several of these projects on summarization [27] Almost all of this work (with the exception of [12, 15, 18, 23]) focused on summarization by text span extraction , with sentences as the most common type of textspan. This technique creates document summaries by concatenating selected text span excerpts from the original document. This paradigm transforms the problem of summarization, which in the most ....
Radev, D., and McKeown, K. Generating natural language summaries from multiple online sources. Compuutational Linguistics (1998).
Sentence Ordering in Multidocument Summarization - Regina Barzilay Department (2001) (3 citations) Self-citation (Mckeown) (Correct)
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D. Radev and K. McKeown. Generating natural language summaries from multiple on-line sources. Computational Linguistics, 24(3):469-500, September 1998.
Towards Generating Patient Specific Summaries of Medical.. - Elhadad, McKeown (2001) Self-citation (Mckeown) (Correct)
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D. Radev and K. McKeown. 1998. Generating natural language summaries from multiple on-line sources. Computational Linguistics, 24(3):469-- 500.
Automatically Learning Cognitive Status for.. - Nenkova, Siddharthan, .. (2005) Self-citation (Mckeown) (Correct)
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D. Radev and K. McKeown. 1998. Generating natural language summaries from multiple on-line sources. Computational Linguistics, 24(3):469--500.
Customization in a Unified Framework for Summarizing.. - Elhadad, Kan.. (2005) Self-citation (Mckeown) (Correct)
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D. Radev and K. McKeown. Generating natural language summaries from multiple on-line sources. Computational Linguistics, 24(3):469--500, September 1998.
Sentence Ordering in Multidocument Summarization - Regina Barzilay Department (2001) (3 citations) Self-citation (Mckeown) (Correct)
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D. Radev and K. McKeown. Generating natural language summaries from multiple on-line sources. Computational Linguistics, ##(3):469-500, September 1998.
Experiments in Single and Multi-Document Summarization - Using Mead Dragomir (2001) (1 citation) Self-citation (Radev) (Correct)
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Dragomir R. Radev and Kathleen R. McKeown. Generating natural language summaries from multiple on-line sources. Computational Linguistics, 24(3):469--500, September 1998.
Experiments in Single and Multi-Document Summarization.. - Radev.. (2001) (1 citation) Self-citation (Radev) (Correct)
....981 U 380 152 129 661 W 363 172 148 683 Y 284 201 205 690 Z 380 209 225 814 Table 3: Multi document evaluation CST proposes a taxonomy of the informational relationships between documents in clusters of related documents. Some of the relationships are direct descendents of these used in SUMMONS [8] except that in CST, these relationships are domain independent. CST posits that by identifying these cross document links , one can produce superior multi document summaries. The concept of using CST for multi document summaries relates to the that of using Rhetorical Structure Theory (RST) 1] ....
Dragomir R. Radev and Kathleen R. McKeown. Generating natural language summaries from multiple on-line sources. Computational Linguistics, 24(3):469--500, September 1998.
Interactive, Domain-Independent Identification and .. - Radev.. Self-citation (Radev) (Correct)
....We conclude the paper with an evaluation of our approach, a discussion of its scalability and portability, and with a glimpse into current work done at our group to extend the functionality of NewsInEssence. 1. 1 Related Work Summarization of multiple documents originated with the SUMMONS system [5, 9]. In it, a series of related stories in a restricted domain were converted to a semantic representation using information extraction and then a summary was produced using natural language generation techniques. Later work on multidocument summarization includes the identi cation of similarities ....
Dragomir R. Radev and Kathleen R. McKeown. Generating natural language summaries from multiple on-line sources. Computational Linguistics, 24(3):469{ 500, September 1998.
Collocations - McKeown, Radev (2000) Self-citation (Radev Mckeown) (Correct)
....words obeying certain constraints, Cook kept only the words in the collocation and thus avoided a combinatorial explosion when several constraints (of collocational or other nature) needed to be combined. Another text generation system that makes use of a specific type of collocations is SUMMONS [33]. In this case, the authors have tried to capture the collocational information linking an entity (person, place, or organization) with its description (pre modifier, apposition, or relative clause) and to use it for generation of referring expressions. For example, if the system discovers that ....
Dragomir R. Radev and Kathleen R. McKeown. Generating natural language summaries from multiple on-line sources. Computational Linguistics, 24(3), September 1998.
Information Extraction and Summarization: Domain Independence.. - Kan, McKeown (1999) (1 citation) Self-citation (Mckeown) (Correct)
....3. The descriptions of individual foci enable the system to nd missing information and add it in. Complementary to work by Jing [Jing1999] whose emphasis is on summary uency, our approach focuses on ensuring summary informativeness. Other work on summarization at Columbia [Barzilay et al..1999, Radev and McKeown1998] focuses on multiple document summarization. In the next section, we describe a classi cation hierarchy of summarization techniques that situates current systems and show how our strategy constitutes a new category. We then illustrate how each of the three tasks above can be accomplished, by ....
....articles are identi ed as belonging to a particular domain. The articles are then summarized by inserting extracted, domain speci c information into a text template, such as a company s name and the amount of its latest dividend. Current e orts in this arena, such as work by Radev and McKeown [Radev and McKeown1998] are considerably more sophisticated, using advanced techniques to dynamically add new text not present in the template. But when no template exists for a story, what then Since there is an in nite variety of domains, we cannot simply exhaustively construct matching templates. Text ....
Dragomir R. Radev and Kathleen R. McKeown. Generating natural language summaries from multiple on-line sources. Computational Linguistics, 24(3):469:500, September 1998.
Information Fusion in the Context of Multi-Document.. - Barzilay, McKeown, Elhadad (1999) (15 citations) Self-citation (Mckeown) (Correct)
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Dragomir R. Radev and Kathleen R. McKeown. 1998. Generating natural language summaries from multiple on-line sources. Computational Linguistics, 24(3):469-500, September.
Summarizing Reports on Evolving Events; - Part Linear Evolution (Correct)
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Dragomir R. Radev. Generating Natural Language Summaries from Multiple On-Line Sources: Language Reuse and Regeneration . Unpublished PhD thesis, Columbia University, 1999.
A Methodology for Extrinsic Evaluation of Text Summarization.. - Dorr, al. (2005) (Correct)
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Dragomir Radev and Kathleen McKeown. 1998. Generating Natural Language Summaries from Multiple On-Line Sources. Computational Linguistics, pages 469--500.
Linear Evolving Summarization: The First Results - Afantenos, Karkaletsis (2004) (Correct)
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Radev, Dragomir R. 1999. "Generating Natural Language Summaries from Multiple On-Line Sources: Language Reuse and Regeneration." Ph.D. diss., Columbia University.
Apprentissage Numrique pour le Rsum de Texte - Massih-Rza Amini Patrick (Correct)
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D. Radev and K. McKeown. Generating natural language summaries from multiple online sources. Computational Linguistics, 1998.
Apprentissage Numrique pour le Rsum de Texte - Massih-Rza Amini Patrick (Correct)
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D. Radev and K. McKeown. Generating natural language summaries from multiple online sources. Computational Linguistics, 1998.
Automatic Extraction of Knowledge from Web Documents - Alani, Kim, Millard, Weal, .. (2003) (Correct)
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Radev, D. R. and K. R. McKeown. "Generating natural language summaries from multiple on-line sources." Compuutational Linguistics 24(3): 469---500, 1998.
Web based Knowledge Extraction and Consolidation.. - Alani, Kim.. (2003) (Correct)
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Radev, D. R., McKeown. K. R.: Generating natural language summaries from multiple on-line sources. Computational Linguistics, 24(3): 469---500, 1998.
Detecting Discrepancies and - Improving Intelligibility Two (2001) (Correct)
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Dragomir R. Radev and Kathleen R. McKeown. Generating natural language summaries from multiple on-line sources. Computational Linguistics, 24(3):469--500, 1988.
Identifying the Interactions of Multi-Criteria in Turkish.. - Yöndem (2001) (Correct)
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Radev, D.R., McKeown, K.R., `Generating Natural Language Summaries from Multiple On-line Sources', Computational Linguistics, Vol. 24, No. 3, 1998.
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