Cross-language document summarization based on machine translation quality prediction

Cross-language document summarization is a task of producing a summary in one language for a document set in a different language. Ex-isting methods simply use machine translation for document translation or summary transla-tion. However, current machine translation services are far from satisfactory, which re-sults in that the quality of the cross-language summary is usually very poor, both in read-ability and content. In this paper, we propose to consider the translation quality of each sen-tence in the English-to-Chinese cross-language summarization process. First, the translation quality of each English sentence in the docu-ment set is predicted with the SVM regression method, and then the quality score of each sen-tence is incorporated into the summarization process. Finally, the English sentences with high translation quality and high informative-ness are selected and translated to form the Chinese summary. Experimental results dem-onstrate the effectiveness and usefulness of the proposed approach.