  We introduce a general framework for abstractive summarization with factual
consistency and distinct modeling of the narrative flow in an output summary.
Our work addresses current limitations of models for abstractive summarization
that often hallucinate information or generate summaries with coherence issues.
  To generate abstractive summaries with factual consistency and narrative
flow, we propose Cooperative Generator -- Discriminator Networks (Co-opNet), a
novel transformer-based framework where a generator works with a discriminator
architecture to compose coherent long-form summaries. We explore four different
discriminator objectives which each capture a different aspect of coherence,
including whether salient spans of generated abstracts are hallucinated or
appear in the input context, and the likelihood of sentence adjacency in
generated abstracts. We measure the ability of Co-opNet to learn these
objectives with arXiv scientific papers, using the abstracts as a proxy for
gold long-form scientific article summaries. Empirical results from automatic
and human evaluations demonstrate that Co-opNet learns to summarize with
considerably improved global coherence compared to competitive baselines.
