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We propose a novel approach for automatic generation of topically-rela ted events from multi-lingual news sources. Named entity terms are extracted automatically from the news content. Together with the content terms, they constitute the basis of representing the story. We employ transformation-based linguistic tagging approach for named entity extraction. Two methods of gross translation on Chinese story representation into English have been implemented. The first approach uses only a bilingual dictionary. The second method makes use of a parallel corpus as an additional resource. Unsupervised learning is employed to discover the events.