Abstract
Text summarization extraction is a natural language processing technology that can quickly extract key information to improve the efficiency of daily transaction processing. The traditional TextRank algorithm is limited by Chinese word segmentation, which results in the low accuracy of Chinese text summarization, and leads to loss important information. This paper proposes an Improved TextRank-based method for Chinese text summarization, which makes full use of the 1-g model analysis in N-Gram to obtain the candidate word vector, and combines the 2-g model and hidden Markov model to achieve part-of-speech tagging and optimized word segmentation. Finally use the improved TextRank model on the optimized word vector to realize the Chinese text summarization extraction. The experimental results show that when the number of summary sentences is no more than 8, the accuracy of Chinese text summary extraction by Improved TextRank-based method is significantly better than traditional TF-IDF and TextRank methods.
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Acknowledgement
This work was supported in part by the Natural Science Foundation of Jiangsu Province under Grant BK20180142, and the Innovation and Entrepreneurship Training Program for College Students in Jiangsu Province (201811460041X).
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Zheng, X., Zhou, T., Wang, Y., Li, S. (2022). An Improved TextRank-Based Method for Chinese Text Summarization. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, vol 13339. Springer, Cham. https://doi.org/10.1007/978-3-031-06788-4_12
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DOI: https://doi.org/10.1007/978-3-031-06788-4_12
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