  Paraphrase generation is an important task in natural language processing.
Previous works focus on sentence-level paraphrase generation, while ignoring
document-level paraphrase generation, which is a more challenging and valuable
task. In this paper, we explore the task of document-level paraphrase
generation for the first time and focus on the inter-sentence diversity by
considering sentence rewriting and reordering. We propose CoRPG (Coherence
Relationship guided Paraphrase Generation), which leverages graph GRU to encode
the coherence relationship graph and get the coherence-aware representation for
each sentence, which can be used for re-arranging the multiple (possibly
modified) input sentences. We create a pseudo document-level paraphrase dataset
for training CoRPG. Automatic evaluation results show CoRPG outperforms several
strong baseline models on the BERTScore and diversity scores. Human evaluation
also shows our model can generate document paraphrase with more diversity and
semantic preservation.
