Neural Network-Based Abstract Generation for Opinions and Arguments

Lu Wang; Wang Ling

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arXiv · 1606.02785

Neural Network-Based Abstract Generation for Opinions and Arguments

Lu Wang·Wang Ling

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Abstract

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.

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BibTeX RIS

Lu Wang, Wang Ling. 2016-06-09. Neural Network-Based Abstract Generation for Opinions and Arguments. https://arxiv.org/abs/1606.02785

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