  Generating long and coherent text is an important but challenging task,
particularly for open-ended language generation tasks such as story generation.
Despite the success in modeling intra-sentence coherence, existing generation
models (e.g., BART) still struggle to maintain a coherent event sequence
throughout the generated text. We conjecture that this is because of the
difficulty for the decoder to capture the high-level semantics and discourse
structures in the context beyond token-level co-occurrence. In this paper, we
propose a long text generation model, which can represent the prefix sentences
at sentence level and discourse level in the decoding process. To this end, we
propose two pretraining objectives to learn the representations by predicting
inter-sentence semantic similarity and distinguishing between normal and
shuffled sentence orders. Extensive experiments show that our model can
generate more coherent texts than state-of-the-art baselines.
