Deep Communicating Agents for Abstractive Summarization [article]

Asli Celikyilmaz, Antoine Bosselut, Xiaodong He, Yejin Choi
<span title="2018-08-15">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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
more &raquo; ... t 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.
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