Fast Abstractive Summarization-RL

Year: 2,018
Journal: Association for Computational Linguistics
Languages: English
Programming languages: Python
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Inspired by how humans summarize long documents, we propose an accurate and fast summarization model that first selects salient sentences and then rewrites them abstractively (i.e., compresses and paraphrases) to generate a concise overall summary. We use a novel sentence-level policy gradient method to bridge the nondifferentiable computation between these two neural networks in a hierarchical way, while maintaining language fluency.

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