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Abstractive summarization incorporating graph knowledge
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DOI：10.1007/s11042-023-17879-1.png)
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摘要
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Automatic text summarization is an important challenge in natural language understanding. Automatic text summarization mainly includes extractive text summarization and abstractive text summarization. Extractive text summarization selects salient content from a document to form a summary, whereas abstractive summaries are formed by generating words and sentences. In this paper, we propose a novel abstractive summarization method incorporating graph knowledge. First, we propose a document word representation model based on a graph convolutional neural network for generating a summary. Then, the graph knowledge is integrated into an abstractive summarization model, which thus gains a better ability to generate new words. Finally, the abstractive summarization model is combined with a pointer generation model to solve the out-of-vocabulary problem. We apply our model to the Xsum and Gigaword summarization datasets, and the experimental results demonstrate that our model achieves state-of-the-art results on the Xsum dataset and results comparable to those of existing methods on the Gigaword dataset.
Keyword:
Abstractive text summarization
Text graph knowledge
Word vector representation
Graph convolutional neural network
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
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