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Abstract: 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 non-differentiable computation between these two neural networks in a hierarchical way, while maintaining language fluency. Empirically, we achieve the new state-of-the-art on all metrics (including human evaluation) on the CNN/Daily Mail dataset, as well as significantly higher abstractiveness scores. Moreover, by first operating at the sentence-level and then the word-level, we enable parallel decoding of our neural generative model that results in substantially faster (10-20x) inference speed as well as 4x faster training convergence than previous long-paragraph encoder-decoder models. We also demonstrate the generalization of our model on the test-only DUC-2002 dataset, where we achieve higher scores than a state-of-the-art model.
Comments: ACL 2018 (17 pages)
Active 2ojNpi4B Subjects: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Active tXOjB Cite as: Round Blue Cotton Shirt T Station Potter Neck Royal Harry Fanideaz Womens 9eLKa
[cs.CL]
  (or arXiv:1805.11080v1 [cs.CL] for this version)

Submission history

From: Yen-Chun Chen [ Heusen Van Brown Shoes Casual o3x7llH
]
[v1] Mon, 28 May 2018 17:49:10 GMT (552kb,D)

Description

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