Towards multidocument summarization by reformulation: progress and prospects
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
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
Journal of Artificial Intelligence Research
Describing and generating multimodal contents featuring affective lifelike agents with MPML
New Generation Computing
Sentence ordering with manifold-based classification in multi-document summarization
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Measuring variability in sentence ordering for news summarization
ENLG '07 Proceedings of the Eleventh European Workshop on Natural Language Generation
An adjacency model for sentence ordering in multi-document summarization
AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology
Aggregation of multiple judgments for evaluating ordered lists
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
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Ordering information is a difficult but a important task for natural language generation applications. A wrong order of information not only makes it difficult to understand, but also conveys an entirely different idea to the reader. This paper proposes an algorithm that learns orderings from a set of human ordered texts. Our model consists of a set of ordering experts. Each expert gives its precedence preference between two sentences. We combine these preferences and order sentences. We also propose two new metrics for the evaluation of sentence orderings. Our experimental results show that the proposed algorithm outperforms the existing methods in all evaluation metrics.