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Multi-document summarization, which summarizes a set of documents with a small number of phrases or sentences, provides a concise and critical essence of the documents. Existing multi-document summarization methods ignore the fact that there often exist many relevant documents that provide surrounding background knowledge, which can help generate a salient and discriminative summary for a given set of documents. In this paper, we propose a novel method, SUMDocS (Surrounding-aware Unsupervised Multi-Document Summarization), which incorporates rich surrounding (topically related) documents to help improve the quality of extractive summarization without human supervision. Speci fically, we propose a joint optimization algorithm to unify global novelty (i.e., category-level frequent and discriminative), local consistency (i.e., locally frequent, co-occurring), and local saliency (i.e., salient from its surroundings) such that the obtained summary captures the characteristics of the target documents. Extensive experiments on news and scientifi c domains demonstrate the superior performance of our method when the unlabeled surrounding corpus is utilized.