Summarization beyond sentence extraction: a probabilistic approach to sentence compression
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Supervised and unsupervised learning for sentence compression
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Discriminative sentence compression with conditional random fields
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Towards effective sentence simplification for automatic processing of biomedical text
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Sentence compression as tree transduction
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A comparison of model free versus model intensive approaches to sentence compression
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
For the sake of simplicity: unsupervised extraction of lexical simplifications from Wikipedia
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
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ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Learning simple Wikipedia: a cogitation in ascertaining abecedarian language
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Entity-focused sentence simplification for relation extraction
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Learning to simplify sentences using Wikipedia
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In this paper we examine the task of sentence simplification which aims to reduce the reading complexity of a sentence by incorporating more accessible vocabulary and sentence structure. We introduce a new data set that pairs English Wikipedia with Simple English Wikipedia and is orders of magnitude larger than any previously examined for sentence simplification. The data contains the full range of simplification operations including rewording, reordering, insertion and deletion. We provide an analysis of this corpus as well as preliminary results using a phrase-based translation approach for simplification.