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      Read, Attend and Comment: A Deep Architecture for Automatic News Comment Generation

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          Abstract

          Automatic news comment generation is beneficial for real applications but has not attracted enough attention from the research community. In this paper, we propose a "read-attend-comment" procedure for news comment generation and formalize the procedure with a reading network and a generation network. The reading network comprehends a news article and distills some important points from it, then the generation network creates a comment by attending to the extracted discrete points and the news title. We optimize the model in an end-to-end manner by maximizing a variational lower bound of the true objective using the back-propagation algorithm. Experimental results on two public datasets indicate that our model can significantly outperform existing methods in terms of both automatic evaluation and human judgment.

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          On the Properties of Neural Machine Translation: Encoder–Decoder Approaches

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            CIDEr: Consensus-based image description evaluation

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              SQuAD: 100,000+ Questions for Machine Comprehension of Text

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                Author and article information

                Journal
                26 September 2019
                Article
                1909.11974
                dc0ac8e6-e996-4969-bdf3-2a79fccfbb98

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                Accepted by EMNLP2019
                cs.CL cs.IR cs.LG

                Theoretical computer science,Information & Library science,Artificial intelligence

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