Abstract
Current popular abstractive summarization is based on an attentional encoder-decoder framework. Based on the architecture, the decoder generates a summary according to the full text that often results in the decoder being interfered by some irrelevant information, thereby causing the generated summaries to suffer from low saliency. Besides, we have observed the process of people writing summaries and find that they write a summary based on the necessary information rather than the full text. Thus, in order to enhance the saliency of the abstractive summarization, we propose an attentive information extraction model. It consists of a multi-layer perceptron (MLP) gated unit that pays more attention to the important information of the source text and a similarity module to encourage high similarity between the reference summary and the important information. Before the summary decoder, the MLP and the similarity module work together to extract the important information for the decoder, thus obtaining the skeleton of the source text. This effectively reduces the interference of irrelevant information to the decoder, therefore improving the saliency of the summary. Our proposed model was tested on CNN/Daily Mail and DUC-2004 datasets, and achieved a 42.01 ROUGE-1 f-score and 33.94 ROUGE-1, recall respectively. The result outperforms the state-of-the-art abstractive model on the same dataset. In addition, by subjective human evaluation, the saliency of the generated summaries was further enhanced.
Highlights
With the rapid development of Internet technology, people are exposed to vast amounts of text information every day such as news, blogs, reports, papers, etc
We can see that our model achieves state-of-the-art results without reinforcement learning
Our target was to enhance the saliency of the summary in abstractive text summarization
Summary
With the rapid development of Internet technology, people are exposed to vast amounts of text information every day such as news, blogs, reports, papers, etc. When we are faced with a large amount of disorganized information, quickly and accurately locating the required information becomes a problem to be solved. Automatic text summarization provides an efficient solution to this task. Text summarization can create a shorter version containing the main idea of the source text automatically. We can judge whether an article is interesting to us based on the shorter version. This can greatly reduce the time consumed in retrieving information
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