This paper presents a method for abstracting multi-keyframe from video datasets. Existing video abstraction methods focused on simple view videos, and the results will be unacceptable if applied to overlapping views directly due to limitations like unavoidable redundancy and complicated inner correlations. We propose a correlation map to naturally model the correlations with various attributes among multi-keyframe, keyframe importance and weighted correlations are then computed to construct the map. The weighted correlations, unlike the unweighted ones, not only model probabilistic relationship among keyframes but also address the temporal and visual similarity. We facilitate the abstraction process via SVM classification and keyframes reduction using rough set. The multi-keyframe correlation map, which serially assembles event-centered keyframes in temporal order, is presented for displaying the abstraction, which shows the correlations and improves the browsability of video datasets.