Inverted indexes are the fundamental index for information retrieval systems. Due to the correlation between terms, inverted lists in the index may have substantial overlap and hence redundancy. In this paper, we propose a new approach that reduces the size of inverted lists while retaining time-efficiency. Our solution is based on merging inverted lists that bear high overlap to each other and manage their content in the resulting condensed index. An efficient algorithm is designed to discover heavily-overlapped inverted lists and construct the condensed index for a given dataset. We demonstrate that our algorithm delivers considerable space saving while incurring little query performance overhead.