Weakly-supervised temporal action localization has attracted much attention among researchers in video content analytics, thanks to its relaxed requirements of video-level annotations instead of frame-level labels. However, many current weakly-supervised action localization methods depend heavily on naive feature combination and empirical thresholds to determine temporal action boundaries, which is practically feasible but could still be sub-optimal. Inspired by the momentum term, we propose a general-purpose action recognition criterion that replaces explicit empirical thresholds. Based on such criterion, we analyze different combination of streams and propose the Action Sensitive Extractor (ASE) that produces action sensitive features. Our ASE sets temporal stream as main stream and extends with complementary spatial streams. We build our Action Sensitive Network (ASN) and evaluate on THUMOS14 and ActivityNet1.2 with different selection method. Our network yields state-of-art performance in both datasets.