Abstractive Document Summarization with a Graph-Based Attentional Neural Model
Jiwei Tan,Xiaojun Wan,Jianguo Xiao +2 more
- Vol. 1, pp 1171-1181
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TLDR
A novel graph-based attention mechanism in the sequence-to-sequence framework to address the saliency factor of summarization, which has been overlooked by prior works and is competitive with state-of-the-art extractive methods.Abstract:
ive summarization is the ultimate goal of document summarization research, but previously it is less investigated due to the immaturity of text generation techniques. Recently impressive progress has been made to abstractive sentence summarization using neural models. Unfortunately, attempts on abstractive document summarization are still in a primitive stage, and the evaluation results are worse than extractive methods on benchmark datasets. In this paper, we review the difficulties of neural abstractive document summarization, and propose a novel graph-based attention mechanism in the sequence-to-sequence framework. The intuition is to address the saliency factor of summarization, which has been overlooked by prior works. Experimental results demonstrate our model is able to achieve considerable improvement over previous neural abstractive models. The data-driven neural abstractive method is also competitive with state-of-the-art extractive methods.read more
Citations
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Proceedings ArticleDOI
Bottom-Up Abstractive Summarization
TL;DR: This work explores the use of data-efficient content selectors to over-determine phrases in a source document that should be part of the summary, and shows that this approach improves the ability to compress text, while still generating fluent summaries.
Proceedings ArticleDOI
Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization
TL;DR: This paper proposed a novel abstractive model which is conditioned on the article's topics and based entirely on convolutional neural networks and demonstrated experimentally that this architecture captures long-range dependencies in a document and recognizes pertinent content, outperforming an oracle extractive system and state-of-theart abstractive approaches when evaluated automatically and by humans.
Proceedings ArticleDOI
Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting
Yen-Chun Chen,Mohit Bansal +1 more
TL;DR: The authors proposed a sentence-level policy gradient method to bridge the non-differentiable computation between these two neural networks in a hierarchical way, which achieved state-of-the-art performance on the CNN/Daily Mail dataset.
Posted Content
Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization
TL;DR: A novel abstractive model is proposed which is conditioned on the article’s topics and based entirely on convolutional neural networks, outperforming an oracle extractive system and state-of-the-art abstractive approaches when evaluated automatically and by humans.
Proceedings ArticleDOI
Ranking Sentences for Extractive Summarization with Reinforcement Learning
TL;DR: The authors conceptualized extractive summarization as a sentence ranking task and proposed a novel training algorithm which globally optimizes the ROUGE evaluation metric through a reinforcement learning objective, which outperforms state-of-the-art extractive and abstractive systems when evaluated automatically and by humans.
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