Accurate and compact representation of music signals is a key component of large-scale content-based music applications such as music content management and near duplicate audio detection. This problem is not well solved yet despite many research efforts in this field. In this paper, we suggest mid-level summarization of music signals based on chord progressions. More specially, in our proposed algorithm, chord progressions are recognized from music signals based on a supervised learning model, and recognition accuracy is improved by locally probing n-best candidates. By investigating the properties of chord progressions, we further calculate a histogram from the probed chord progressions as a summary of the music signal. We show that the chord progression-based summarization is a powerful feature descriptor for representing harmonic progressions and tonal structures of music signals. The proposed algorithm is evaluated with content-based music retrieval as a typical application. The experimental results on a dataset with more than 70,000 songs confirm that our algorithm can effectively improve summarization accuracy of musical audio contents and retrieval performance, and enhance music retrieval applications on large-scale audio databases.