Current Automatic Text Simplification (TS) work relies on sequence-to-sequence neural models that learn simplification operations from parallel complex-simple corpora. In this paper we address three open challenges in these approaches: (i) avoiding unnecessary transformations, (ii) determining which operations to perform, and (iii) generating simplifications that are suitable for a given target audience. For (i), we propose joint and two-stage approaches where instances are marked or classified as simple or complex. For (ii) and (iii), we propose fusion-based approaches to incorporate information on the target grade level as well as the types of operation to perform in the models. While grade-level information is provided as metadata, we devise predictors for the type of operation. We study different representations for this information as well as different ways in which it is used in the models. Our approach outperforms previous work on neural TS, with our best model following the two-stage approach and using the information about grade level and type of operation to initialise the encoder and the decoder, respectively.