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SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
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Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
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Journal of the ACM (JACM)
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Information Processing and Management: an International Journal
Automatic evaluation of summaries using N-gram co-occurrence statistics
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
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Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
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Proceedings of the 18th international conference on World wide web
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EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
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ILP '09 Proceedings of the Workshop on Integer Linear Programming for Natural Langauge Processing
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ILP '09 Proceedings of the Workshop on Integer Linear Programming for Natural Langauge Processing
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IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
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ECIR'07 Proceedings of the 29th European conference on IR research
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ECDL'07 Proceedings of the 11th European conference on Research and Advanced Technology for Digital Libraries
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ACM Transactions on Speech and Language Processing (TSLP)
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This paper presents a comparative study on two key problems existing in extractive summarization: the ranking problem and the selection problem. To this end, we presented a systematic study of comparing different learning-to-rank algorithms and comparing different selection strategies. This is the first work of providing systematic analysis on these problems. Experimental results on two benchmark datasets demonstrate three findings: (1) pairwise and listwise learning-to-rank algorithms outperform the baselines significantly; (2) there is no significant difference among the learning-to-rank algorithms; and (3) the integer linear programming selection strategy generally outperformed Maximum Marginal Relevance and Diversity Penalty strategies.