Algorithms for clustering data
Algorithms for clustering data
Cross-document summarization by concept classification
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Cranking: Combining Rankings Using Conditional Probability Models on Permutations
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Sentence ordering in multidocument summarization
HLT '01 Proceedings of the first international conference on Human language technology research
Probabilistic text structuring: experiments with sentence ordering
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Syntactic simplification for improving content selection in multi-document summarization
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Inferring strategies for sentence ordering in multidocument news summarization
Journal of Artificial Intelligence Research
A machine learning approach to sentence ordering for multidocument summarization and its evaluation
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
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In this paper, we proposed a new method named adjacency based ordering to order sentences for summarization tasks. Given a group of sentences to be organized into the summary, connectivity of each pair of sentences is learned from source documents. Then a top-first strategy is implemented to define the sentence ordering. It provides a solution of ordering texts while other information except the source documents is not available. We compared this method with other existing sentence ordering methods. Experiments and evaluations are made on data collection of DUC04. The results show that this method distinctly outperforms other existing sentence ordering methods. Its low input requirement also makes it capable to most summarization and text generation tasks.