ACM Computing Surveys (CSUR)
Summarization beyond sentence extraction: a probabilistic approach to sentence compression
Artificial Intelligence
Example-based sentence reduction using the hidden markov model
ACM Transactions on Asian Language Information Processing (TALIP)
Learning to paraphrase: an unsupervised approach using multiple-sequence alignment
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Bootstrapping lexical choice via multiple-sequence alignment
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Unsupervised construction of large paraphrase corpora: exploiting massively parallel news sources
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Trimming CFG parse trees for sentence compression using machine learning approaches
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Unsupervised induction of sentence compression rules
UCNLG+Sum '09 Proceedings of the 2009 Workshop on Language Generation and Summarisation
Paraphrase alignment for synonym evidence discovery
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Terminological paraphrase extraction from scientific literature based on predicate argument tuples
Journal of Information Science
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In this paper, we present a study for extracting and aligning paraphrases in the context of Sentence Compression. First, we justify the application of a new measure for the automatic extraction of paraphrase corpora. Second, we discuss the work done by (Barzilay & Lee, 2003) who use clustering of paraphrases to induce rewriting rules. We will see, through classical visualization methodologies (Kruskal & Wish, 1977) and exhaustive experiments, that clustering may not be the best approach for automatic pattern identification. Finally, we will provide some results of different biology based methodologies for pairwise paraphrase alignment.