Neural Network-Based Abstract Generation for Opinions and Arguments. (arXiv:1606.02785v1 [cs.CL])
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 from an important subset of input.