We present deep communicating agents in an encoder-decoder architecture to address the challenges of representing a long document for abstractive summarization.

Mar 27, 2018 · Empirical results demonstrate that multiple communicating encoders lead to a higher quality summary compared to several strong baselines, ...

We present deep communicating agents in an encoder-decoder architecture to address the challenges of representing a long document for abstractive summarization.

Empirical results demonstrate that multiple communicating encoders lead to a higher quality summary compared to several strong baselines, including those ...

Multi-Agent: Malcolm Turnbull was set to run for prime minister if. Tony Abbott had been successfully toppled in February's leadership spill. He is set to.

PyTorch implementation of Deep Communicating Agents for Abstractive Summarization. License. MIT license · 8 stars 2 forks Branches Tags Activity.

The RL training process typically involves two steps: (1) sampling a number of candidate sequences with a pretrained model given an input (call it ...

TensorFlow implementation of Deep Communicating Agents for Abstractive Summarization. 7 stars 1 fork Branches Tags Activity.

Aug 15, 2018 · With deep communicating agents, the task of encoding a long text is divided across multiple collaborating agents, each in charge of a subsection ...

We present a novel abstractive document summarization based on the recently proposed dynamic convolutional encoder-decoder architectures.