Generating indicative-informative summaries with sumUM
Computational Linguistics - Summarization
Generating natural language summaries from multiple on-line sources
Computational Linguistics - Special issue on natural language generation
Cut and paste based text summarization
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
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
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
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EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
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ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
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NAACL-ANLP-AutoSum '00 Proceedings of the 2000 NAACL-ANLP Workshop on Automatic Summarization
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EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
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Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Graph-based informative-sentence selection for opinion summarization
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Compact explanatory opinion summarization
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Proceedings of the 2013 workshop on Living labs for information retrieval evaluation
Generation of description metadata for video files
Proceedings of the 14th International Conference on Computer Systems and Technologies
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We present a novel graph-based summarization framework (Opinosis) that generates concise abstractive summaries of highly redundant opinions. Evaluation results on summarizing user reviews show that Opinosis summaries have better agreement with human summaries compared to the baseline extractive method. The summaries are readable, reasonably well-formed and are informative enough to convey the major opinions.