Due to the various appearances of scene text instances and the disturbance of background, it is still a challenging task to design an effective and accurate text detector. To tackle this problem, in this paper we propose a novel dual-stream scene text detector considering semantic compensation and feature interaction. The detector extracts image features from two input images of different resolution, which improves its perceptive ability and contributes to detecting large and long texts. Specifically, we propose a Semantic Compensation Module (SCM) to aggregate features between the two streams, which compensates semantic information in features at each level via an attention mechanism. Moreover, we design a Feature Interaction Module (FIM) to obtain more expressive features. Experiments conducted on three benchmark datasets, ICDAR2015, MSRA-TD500 and ICDAR2017-MLT, demonstrate that our proposed method has competitive performance and strong robustness.