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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)
Slim Blue Cotton England shirts Formal Peter WnHiAYTp Subjects: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Submission history

From: Yen-Chun Chen [ Coat Women Cloak Breasted Pockets Outdoor Stand Coat Casual Double Collar Solid oabXeezCo
]
[v1] Mon, 28 May 2018 17:49:10 GMT (552kb,D)

Product Details

Brand
Peter England
Product Code
APPPETER-ENGLANADIT50996404D16FA
Color
White
Size
40
Material
Cotton
Occasion
Semi Formal
Pattern
Solid
Sleeve
Half Sleeves
Neck Type
Classic Collar
Fit
Slim
Gender
Men
Collar Type
Classic Collar
Article Identifier Type
EAN CODE
Article Identification Value
8907696262397
Style Code
ESF51702328