Deep generative models are a relatively more advanced topic in DL, if you want to pursue this route to generate sequence data, text in your case, here are some useful papers:
Variational Autoencoder (VAE) based: https://arxiv.org/pdf/1511.06349.pdf, https://arxiv.org/pdf/1511.06038.pdf
Generative Adversarial Network (GAN) based: https://arxiv.org/pdf/1609.05473.pdf [SeqGAN]

Another route you can go about this is seq2seq language models, which should be a lot easier to build/train. To generate samples, you would need to feed some initial token. But that can be sampled by a distribution estimated with training data.