Recently, scene text detection based on deep learning has witnessed rapid development. However, most previous methods suffer from (i) semantic information diluted, (ii) false positives in their detections. To alleviate the above dilemmas, we propose an end-to-end trainable text detector named Semantic-compensated and Attention-guided Network (SANet). It contains a Semantic Compensation Module (SCM) and a Text Attention Module (TAM). Specifically, SCM aims to alleviate the dilution of semantic information via compensating the semantic information to the features at each level in a top-down pathway. Furthermore, TAM is employed to encode the strong supervised information into convolutional features, where the text-related features are significantly enhanced by increasing the response gap between text and background. The experimental results on three benchmark datasets, ICDAR2015, MSRA-TD500, and ICDAR2017-MLT prove the effectiveness of our method.