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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 17 of 17 1 Gender differences across correlated corpora: preliminary results Roberta E. Sabin; Kerri A. Goodwin; Jade Goldstein-stewart... In: http://www.aaai.org/Papers/FLAIRS/2008/FLAIRS08-052.pdf (2008) BASE Show details 2 Genre identification and goal-focused summarization Jade Goldstein; Gary M. Ciany; Jaime G. Carbonell In: http://www.cs.fiu.edu/%7Elli003/Sum/CIKM/2007/p889-goldstein.pdf (2007) BASE Show details 3 c○2006 The Association for Computational Linguistics Tat-seng Chua; Jade Goldstein; Simone Teufel... In: http://aclweb.org/anthology//W/W06/W06-07.pdf (2006) BASE Show details 4 CLASSY Query-Based Multi-Document Summarization John M. Conroy; Jade Goldstein Stewart; Judith D. Schlesinger In: http://duc.nist.gov/pubs/2005papers/ida.conroy.pdf (2005) BASE Show details 5 Left-Brain/Right-Brain Multi-Document Summarization John M. Conroy; Jade Goldstein; Judith D. Schlesinger... In: http://www.cs.umd.edu/users/oleary/reprints/c32.pdf (2004) BASE Show details 6 Creating and Evaluating Multi-Document Sentence Extract Summaries Jade Goldstein; Vibhu Mittal; Jaime Carbonell... In: http://www.cs.cmu.edu/~callan/Papers/goldstein-cikm00.pdf (2000) BASE Show details 7 Creating and Evaluating Multi-Document Sentence Extract Summaries Jade Goldstein; Jade Goldstein; Vibhu Mittal... In: http://www.cs.cmu.edu/~jade/ps/cikm00.ps (2000) BASE Show details 8 Summarizing text documents: Sentence selection and evaluation metrics Jade Goldstein; Mark Kantrowitz; Vibhu Mittal. - 1999 BASE Show details 9 Selecting Text Spans for Document Summaries: Heuristics and Metrics Vibhu Mittal; Mark Kantrowitz; Jade Goldstein... In: http://www.cs.cmu.edu/~mittal/abstracts/aaai99.ps.gz (1999) BASE Show details 10 Summarizing Text Documents: Sentence Selection and Evaluation Metrics Jade Goldstein; Mark Kantrowitz; Vibhu Mittal... In: http://www.cs.cmu.edu/~jade/ps/sigir99.ps (1999) BASE Show details 11 Selecting text spans for document summaries: Heuristics and metrics Vibhu Mittal; Mark Kantrowitz; Jade Goldstein... In: https://www.aaai.org/Papers/AAAI/1999/AAAI99-067.pdf (1999) BASE Show details 12 Generating Extraction-Based Summaries from Hand-Written Summaries by Aligning Text Spans Michele Banko; Vibhu Mittal; Mark Kantrowitz... In: http://www.jprc.com/publications/xtract/xtract.ps.gz (1999) BASE Show details 13 Summarizing Text Documents: Sentence Selection and Evaluation Metrics Jade Goldstein; Mark Kantrowitz; Vibhu Mittal... In: http://www-connex.lip6.fr/~amini/./RelatedWorks/Gol99.ps.gz (1999) BASE Show details 14 Summarizing Text Documents: Sentence Selection and Evaluation Metrics Jade Goldstein; Mark Kantrowitz; Vibhu Mittal... In: http://ranger.uta.edu/~alp/ix/readings/GoldsteinSigir99NewsSummarization.pdf (1999) BASE Show details 15 The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries Jaime Carbonell; Jade Goldstein In: http://www.cs.cmu.edu/~jade/ps/sigir98.ps (1998) Abstract: This paper presents a method for combining query-relevance with information-novelty in the context of text retrieval and summarization. The Maximal Marginal Relevance (MMR) criterion strives to reduce redundancy while maintaining query relevance in re-ranking retrieved documents and in selecting appropriate passages for text summarization. Preliminary results indicate some benefits for MMR diversity ranking in document retrieval and in single document summarization. The latter are borne out by the recent results of the SUMMAC conference in the evaluation of summarization systems. However, the clearest advantage is demonstrated in constructing non-redundant multi-document summaries, where MMR results are clearly superior to non-MMR passage selection. 1 Introduction With the continuing growth of online information, it has become increasingly important to provide improved mechanisms to find information quickly. Conventional IR systems rank and assimilate documents based on maximizing re. URL: http://www.cs.cmu.edu/~jade/ps/sigir98.ps http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.50.2490 BASE Hide details 16 The use of MMR, diversity-based reranking for reordering documents and producing summaries Jaime Carbonell; Jade Goldstein In: http://www.cs.cmu.edu/%7Ejgc/publication/The_Use_MMR_Diversity_Based_LTMIR_1998.pdf (1998) BASE Show details 17 Annotating Subsets of the Enron Email Corpus Jade Goldstein; Andres Kwasinski; Paul Kingsbury... In: http://www.ceas.cc/2006/13.pdf BASE Show details

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