Abstract
Neural sequence-to-sequence model has achieved great success in abstractive summarization task. However, due to the limit of input length, most of previous works can only utilize lead sentences as the input to generate the abstractive summarization, which ignores crucial information of the document. To alleviate this problem, we propose a novel approach to improve neural sentence summarization by using extractive summarization, which aims at taking full advantage of the document information as much as possible. Furthermore, we present both of streamline strategy and system combination strategy to achieve the fusion of the contents in different views, which can be easily adapted to other domains. Experimental results on CNN/Daily Mail dataset demonstrate both our proposed strategies can significantly improve the performance of neural sentence summarization.
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Acknowledgments
The research work has been funded by the Natural Science Foundation of China under Grant No. 61673380, No. 61402478 and No. 61403379.
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Zhu, J., Zhou, L., Li, H., Zhang, J., Zhou, Y., Zong, C. (2018). Augmenting Neural Sentence Summarization Through Extractive Summarization. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2017. Lecture Notes in Computer Science(), vol 10619. Springer, Cham. https://doi.org/10.1007/978-3-319-73618-1_2
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DOI: https://doi.org/10.1007/978-3-319-73618-1_2
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