Neural Network-Based Abstract Generation for Opinions and Arguments

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.

arXiv commentNAACL 2016

References in corpus (6)

• Efficient Estimation of Word Representations in Vector Space
• Neural Machine Translation by Jointly Learning to Align and Translate
• Sequence to Sequence Learning with Neural Networks
• Teaching Machines to Read and Comprehend
• Query-Focused Opinion Summarization for User-Generated Content
• Abstractive Multi-Document Summarization via Phrase Selection and Merging

Cited by in corpus (4)

• Graph-based Neural Multi-Document Summarization
• Paper Abstract Writing through Editing Mechanism
• Robust Neural Abstractive Summarization Systems and Evaluation against Adversarial Information
• Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised