Centering: a framework for modeling the local coherence of discourse
Computational Linguistics
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Introduction to the special issue on summarization
Computational Linguistics - Summarization
Cranking: Combining Rankings Using Conditional Probability Models on Permutations
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
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
Probabilistic text structuring: experiments with sentence ordering
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Inferring strategies for sentence ordering in multidocument news summarization
Journal of Artificial Intelligence Research
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Ordering information is a critical task for multi-document summarization(MDS) because it heavily influent the coherence of the generated summary. In this paper, we propose a hybrid model for sentence ordering in extractive multi-document summarization that combines four relations between sentences – chronological relation, positional relation, topical relation and dependent relation. This model regards sentence as vertex and combined relation as edge of a directed graph on which the approximately optimal ordering can be generated with PageRank analysis. Evaluation of our hybrid model shows a significant improvement of the ordering over strategies losing some relations and the results also indicate that this hybrid model is robust for articles with different genre.