Support Vector Machines for Query-focused Summarization trained and evaluated on Pyramid data
Maria Fuentes,Enrique Alfonseca,Horacio Rodríguez +2 more
- 25 Jun 2007
- pp 57-60
TL;DR: This paper presents the use of Support Vector Machines to detect relevant information to be included in a query-focused summary, using both ROUGE and autoPan, an automatic scoring method for pyramid evaluation.
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Abstract: This paper presents the use of Support Vector Machines (SVM) to detect relevant information to be included in a query-focused summary. Several SVMs are trained using information from pyramids of summary content units. Their performance is compared with the best performing systems in DUC-2005, using both ROUGE and autoPan, an automatic scoring method for pyramid evaluation.
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A Sentence Compression Based Framework to Query-Focused Multi-Document Summarization
Lu Wang,Hema Raghavan,Vittorio Castelli,Radu Florian,Claire Cardie +4 more
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References
One-class svms for document classification
Larry M. Manevitz,Malik Yousef +1 more
TL;DR: The SVM approach as represented by Schoelkopf was superior to all the methods except the neural network one, where it was, although occasionally worse, essentially comparable.
Automatic Evaluation of Machine Translation Quality Using Longest Common Subsequence and Skip-Bigram Statistics
Chin-Yew Lin,Franz Josef Och +1 more
- 21 Jul 2004
TL;DR: Two new objective automatic evaluation methods for machine translation based on longest common subsequence between a candidate translation and a set of reference translations and relaxes strict n-gram matching to skip-bigram matching are described.
Evaluating Content Selection in Summarization: The Pyramid Method
Ani Nenkova,Rebecca J. Passonneau +1 more
- 01 Jan 2004
TL;DR: It is argued that the method presented is reliable, predictive and diagnostic, thus improves considerably over the shortcomings of the human evaluation method currently used in the Document Understanding Conference.
Query-focused summarization by supervised sentence ranking and skewed word distributions
Brian Roark
- 01 Jan 2006
TL;DR: Empirical trials on the DUC 2006 query-directed multi-document summarization task are presented, and it is demonstrated that the very general machine learning approaches taken can provide competitive results for this task.
The Embra System at DUC 2005: Query-oriented Multi-document Summarization with a Very Large Latent Semantic Space
Ben Hachey,Gabriel Murray,David Reitter +2 more
- 01 Jan 2005
TL;DR: The Embra system is presented, a rst-time entry to DUC for 2005 which performed at or above median for the manual assessment of responsiveness and on 4 out of 5 linguistic quality questions.
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