Retrieving similar documents from a large-scale text corpus according to a given document is a fundamental technique for many applications. However, most of existing indexing techniques have diﬃculties to address this problem due to special properties of a document query, e.g. high dimensionality , sparse representation and semantic issue . Towards addressing this problem, we propose a two-level retrieval solution based on a document decomposition idea. A document is decomposed to a compact vector and a few document speciﬁc keywords by a dimension reduction approach. The compact vector embodies the major semantics of a document, and the document speciﬁc keywords complement the discriminative power lost in dimension reduction process. We adopt locality sensitive hashing (LSH) to index the compact vectors, which guarantees to quickly ﬁnd a set of related documents according to the vector of a query document. Then we re-rank documents in this set by their document speciﬁc keywords. In experiments, we obtained promising results on various datasets in terms of both accuracy and performance. We demonstrated that this solution is able to index large-scale corpus for eﬃcient similarity-based document retrieval.