Computational Linguistics
Generating natural language summaries from multiple on-line sources
Computational Linguistics - Special issue on natural language generation
Message Understanding Conference-6: a brief history
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Japanese dependency analysis using cascaded chunking
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Extractive summarization using inter- and intra- event relevance
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Preemptive information extraction using unrestricted relation discovery
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
LexRank: graph-based lexical centrality as salience in text summarization
Journal of Artificial Intelligence Research
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Currently, a lot of news articles are published on the Web, and it is getting easier for us to read them. However, the number of articles are too large for us to read all of them. Although some Web sites cluster/classify news articles into some topics (categories), it is not enough since a large number of articles are still in each topic. Detecting difference between articles on one topic will be one of the solution to comprehend the whole topic. In this paper, we propose a method for detection of difference between news articles on the same topic. Articles are sequentially compared by three different comparison units: paragraphs, sentences, and simple sentences. Our method is evaluated by applying it to Japanese news articles.