This paper proposes an approach KeyRank to extract high quality keyphrases from a document in English. It firstly searches all keyphrase candidates from the document, and then ranks them for selecting top-N keyphrase candidates as final keyphrases. Based on a common sense that words do not repeat-edly appear in an effective keyphrase in English, a novel keyphrase candidate search algorithm applying sequential pat-tern mining with gap constraints (called KCSP) is proposed to search keyphrase candidates for KeyRank. An effectiveness eval-uation measure pattern frequency with entropy (called PF-H) is then proposed to rank these keyphrase candidates for KeyRank. Our experimental results show that KeyRank performs better than existing popular approaches do, such as TextRank and KeyEx. Besides, KCSP is much more efficient than a closely re-lated approach SPMW, and PF-H can be applied to improve the performance of TextRank.