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. Automatic evaluation indicates that our system outperforms state-of-the-art abstractive and extractive summarization systems on two newly collected datasets of movie reviews and arguments. Our system summaries are also rated as more informative and grammatical in human evaluation.
@article{arxiv.1606.02785,
title = {Neural Network-Based Abstract Generation for Opinions and Arguments},
author = {Lu Wang and Wang Ling},
journal= {arXiv preprint arXiv:1606.02785},
year = {2016}
}