Cross-language document summarization is defined as the task of producing a summary in a target language (e.g. Chinese) for a set of documents in a source language (e.g. English). Existing methods for addressing this task make use of either the information from the original documents in the source language or the information from the translated documents in the target language. In this study, we propose to use the bilingual information from both the source and translated documents for this task. Two summarization methods (SimFusion and CoRank) are proposed to leverage the bilingual in-formation in the graph-based ranking framework for cross-language summary extraction. Experimental results on the DUC2001 dataset with manually translated reference Chinese summaries show the effectiveness of the proposed methods.