  We present deep communicating agents in an encoder-decoder architecture to
address the challenges of representing a long document for abstractive
summarization. With deep communicating agents, the task of encoding a long text
is divided across multiple collaborating agents, each in charge of a subsection
of the input text. These encoders are connected to a single decoder, trained
end-to-end using reinforcement learning to generate a focused and coherent
summary. Empirical results demonstrate that multiple communicating encoders
lead to a higher quality summary compared to several strong baselines,
including those based on a single encoder or multiple non-communicating
encoders.
