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Gender differences across correlated corpora: preliminary results
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In: http://www.aaai.org/Papers/FLAIRS/2008/FLAIRS08-052.pdf (2008)
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Genre identification and goal-focused summarization
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In: http://www.cs.fiu.edu/%7Elli003/Sum/CIKM/2007/p889-goldstein.pdf (2007)
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c○2006 The Association for Computational Linguistics
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In: http://aclweb.org/anthology//W/W06/W06-07.pdf (2006)
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| 4 |
CLASSY Query-Based Multi-Document Summarization
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In: http://duc.nist.gov/pubs/2005papers/ida.conroy.pdf (2005)
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| 5 |
Left-Brain/Right-Brain Multi-Document Summarization
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In: http://www.cs.umd.edu/users/oleary/reprints/c32.pdf (2004)
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| 6 |
Creating and Evaluating Multi-Document Sentence Extract Summaries
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In: http://www.cs.cmu.edu/~callan/Papers/goldstein-cikm00.pdf (2000)
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| 7 |
Creating and Evaluating Multi-Document Sentence Extract
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In: http://www.cs.cmu.edu/~jade/ps/cikm00.ps (2000)
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| 8 |
Summarizing text documents: Sentence selection and evaluation metrics
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| 9 |
Selecting Text Spans for Document Summaries: Heuristics and Metrics
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In: http://www.cs.cmu.edu/~mittal/abstracts/aaai99.ps.gz (1999)
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Abstract:
Human-quality text summarization systems are difficult to design, and even more difficult to evaluate, in part because documents can differ along several dimensions, such as length, writing style and lexical usage. Nevertheless, certain cues can often help suggest the selection of sentences for inclusion in a summary. This paper presents an analysis of news-article summaries generated by sentence extraction. Sentences are ranked for potential inclusion in the summary using a weighted combination of linguistic features -- derived from an analysis of news-wire summaries. This paper evaluates the relative effectiveness of these features. In order to do so, we discuss the construction of a large corpus of extraction-based summaries, and characterize the underlying degree of difficulty of summarization at different compression levels on articles in this corpus. Results on our feature set are presented after normalization by this degree of difficulty.
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URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.43.2286
http://www.cs.cmu.edu/~mittal/abstracts/aaai99.ps.gz
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| 10 |
Summarizing Text Documents: Sentence Selection and Evaluation Metrics
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In: http://www.cs.cmu.edu/~jade/ps/sigir99.ps (1999)
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| 11 |
Selecting text spans for document summaries: Heuristics and metrics
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In: https://www.aaai.org/Papers/AAAI/1999/AAAI99-067.pdf (1999)
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| 12 |
Generating Extraction-Based Summaries from Hand-Written Summaries by Aligning Text Spans
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In: http://www.jprc.com/publications/xtract/xtract.ps.gz (1999)
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| 13 |
Summarizing Text Documents: Sentence Selection and Evaluation Metrics
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In: http://www-connex.lip6.fr/~amini/./RelatedWorks/Gol99.ps.gz (1999)
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| 14 |
Summarizing Text Documents: Sentence Selection and Evaluation Metrics
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In: http://ranger.uta.edu/~alp/ix/readings/GoldsteinSigir99NewsSummarization.pdf (1999)
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| 15 |
The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries
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In: http://www.cs.cmu.edu/~jade/ps/sigir98.ps (1998)
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| 16 |
The use of MMR, diversity-based reranking for reordering documents and producing summaries
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In: http://www.cs.cmu.edu/%7Ejgc/publication/The_Use_MMR_Diversity_Based_LTMIR_1998.pdf (1998)
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| 17 |
Annotating Subsets of the Enron Email Corpus
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In: http://www.ceas.cc/2006/13.pdf
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