A bottom-up approach to sentence ordering for multi-document summarization

Author:

Bollegala Danushka,Okazaki Naoaki,Ishizuka Mitsuru

Publisher

Elsevier BV

Subject

Library and Information Sciences,Management Science and Operations Research,Computer Science Applications,Media Technology,Information Systems

Reference30 articles.

1. Barzilay, R., & Lee, L. (2004). Catching the drift: Probabilistic content models, with applications to generation and summarization. In HLT-NAACL 2004: Proceedings of the main conference (pp. 113–120).

2. Inferring strategies for sentence ordering in multidocument news summarization;Barzilay;Journal of Artificial Intelligence Research,2002

3. Carbonell, J., & Goldstein, J. (1998). The use of MMR, diversity-based reranking for reordering documents and producing summaries. In Proceedings of the 21st annual international ACM SIGIR conference on research and development in information retreival (pp. 335–336).

4. Duboue, P., & McKeown, K. (2001). Empirically estimating order constraints for content planning in generation. In Proceedings of the 39th annual meeting of the association for computational linguistics (ACL’01) (pp. 172–179).

5. Duboue, P., & McKeown, K. (2002). Content planner construction via evolutionary algorithms and a corpus-based fitness function. In Proceedings of the second international natural language generation conference (INLG’02) (pp. 89–96).

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