The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Finding topic words for hierarchical summarization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Statistics-Based Summarization - Step One: Sentence Compression
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
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
Inferring strategies for sentence ordering in multidocument news summarization
Journal of Artificial Intelligence Research
Improving query focused summarization using look-ahead strategy
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Answering opinion questions on products by exploiting hierarchical organization of consumer reviews
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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This paper introduces a novel hierarchical summarization approach for automatic multi-document summarization. By creating a hierarchical representation of the words in the input document set, the proposed approach is able to incorporate various objectives of multi-document summarization through an integrated framework. The evaluation is conducted on the DUC 2007 data set.