A Mutilingual News Summarizer

Abstract

Huge multilingual news articles are reported and disseminated on the Internet. How to extract the key information and save the reading time is a crucial issue. This paper proposes architecture of multilingual news summarizer, including monolingual and multilingual clustering, similarity measure among meaningful units, and presentation of summarization results. Translation among news stories, idiosyncrasy among languages, implicit information, and user preference are addressed.