A framework of a mechanical translation between Japanese and English by analogy principle
Proc. of the international NATO symposium on Artificial and human intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
Learning translation templates from examples
Information Systems - Special issue on selected papers from 6th annual workshop on information technologies and systems, December 1996, Cleveland, Ohio, USA
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Advances in Automatic Text Summarization
Advances in Automatic Text Summarization
Learning Translation Templates from Bilingual Translation Examples
Applied Intelligence
Summarization beyond sentence extraction: a probabilistic approach to sentence compression
Artificial Intelligence
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Sentence reduction for automatic text summarization
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Less is more: eliminating index terms from subordinate clauses
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Improving summarization performance by sentence compression: a pilot study
AsianIR '03 Proceedings of the sixth international workshop on Information retrieval with Asian languages - Volume 11
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Sentence Compression by Removing Recursive Structure from Parse Tree
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Sentence compression as tree transduction
Journal of Artificial Intelligence Research
Biology based alignments of paraphrases for sentence compression
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
Unsupervised induction of sentence compression rules
UCNLG+Sum '09 Proceedings of the 2009 Workshop on Language Generation and Summarisation
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Sentence reduction is the removal of redundant words or phrases from an input sentence by creating a new sentence in which the gist of the original meaning of the sentence remains unchanged. All previous methods required a syntax parser before sentences could be reduced; hence it was difficult to apply them to a language with no reliable parser. In this article we propose two new sentence-reduction algorithms that do not use syntactic parsing for the input sentence. The first algorithm, based on the template-translation learning algorithm, one of example-based machine-translation methods, works quite well in reducing sentences, but its computational complexity can be exponential in certain cases. The second algorithm, an extension of the template--translation algorithm via innovative employment of the Hidden Markov model, which uses the set of template rules learned from examples, can overcome this computation problem. Experiments show that the proposed algorithms achieve acceptable results in comparison to sentence reduction done by humans.