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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)
White Peter England Shirt tZ3Awyn Subjects: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Submission history

From: Yen-Chun Chen [ view email ]
[v1] Mon, 28 May 2018 17:49:10 GMT (552kb,D)

Description

Savor a smart beginning to your corporate career wearing this white shirt from Peter England. Tailored in a slim fit;this design is delicately crafted from cotton. Style this full sleeve shirt with grey trousers and Derbies to finish your look.
Brand
Peter England
Product Code
APPPETER-ENGLANADIT5099669927FBF
Color
White
Size
42
Material
Cotton
Occasion
Formal
Pattern
Solid
Sleeve
Full Sleeves
Neck Type
Classic Collar
Fit
Slim
Gender
Men
Collar Type
Classic Collar
Set Contents
1
Wash Care
Machine Wash
Article Identifier Type
Ean Code
Article Identification Value
8907696728954
Style Code
ESF517013480