Neural Network-Based Abstract Generation for Opinions and Arguments
HLT-NAACL, pp. 47-57, 2016.
We study the problem of generating abstractive summaries for opinionated text. We propose an attention-based neural network model that is able to absorb information from multiple text units to construct informative, concise, and fluent summaries. An importance-based sampling method is designed to allow the encoder to integrate information...More
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