Information fusion for multidocument summarization: paraphrasing and generation
Information fusion for multidocument summarization: paraphrasing and generation
Probabilistic text structuring: experiments with sentence ordering
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Robust, applied morphological generation
INLG '00 Proceedings of the first international conference on Natural language generation - Volume 14
A bottom-up approach to sentence ordering for multi-document summarization
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Modeling local coherence: An entity-based approach
Computational Linguistics
Sentence ordering with manifold-based classification in multi-document summarization
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Evaluating Centering for sentence ordering in two new domains
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
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We present a shallow approach to the sentence ordering problem. The employed features are based on discourse entities, shallow syntactic analysis, and temporal precedence relations retrieved from VerbOcean. We show that these relatively simple features perform well in a machine learning algorithm on datasets containing sequences of events, and that the resulting models achieve optimal performance with small amounts of training data. The model does not yet perform well on datasets describing the consequences of events, such as the destructions after an earthquake.