  We propose a new framework for abstractive text summarization based on a
sequence-to-sequence oriented encoder-decoder model equipped with a deep
recurrent generative decoder (DRGN).
  Latent structure information implied in the target summaries is learned based
on a recurrent latent random model for improving the summarization quality.
  Neural variational inference is employed to address the intractable posterior
inference for the recurrent latent variables.
  Abstractive summaries are generated based on both the generative latent
variables and the discriminative deterministic states.
  Extensive experiments on some benchmark datasets in different languages show
that DRGN achieves improvements over the state-of-the-art methods.
