Automatic Text Summary Method Based on Optimized K-Means Clustering Algorithm with Symmetry and Maximal-Marginal-Relevance Algorithm

Abstract

Text summary is an information processing technology that aims to extract the important information in the text and filter out the useless information. In the research literature, text summary methods generate a text summary by clustering, supervised-based, and unsupervised-based methods. However, the K value selection of K-means clustering algorithms is manually specified, and the improper selection of the K value will lead to a poor clustering effect. At the same time, most automatic text summary methods have high redundancy. To solve the above problems, this paper proposes an automatic text summary method based on an optimized K-means clustering algorithm with symmetry and the Maximal-Marginal-Relevance (MMR) algorithm. This method uses the Genetic Algorithm with symmetry to optimize the K value selection of the K-means clustering algorithm and reduces the sentence redundancy of the text summary by using the Maximal-Marginal-Relevance algorithm. The experimental results show that the three evaluation indicators of the proposed method, namely, ROUGE-1, ROUGE-2, and ROUGE-L, have increased by an average of 96.81%, 2.39 times, and 10.15%, respectively, compared with the other three automatic text summary methods, including Lead-3, Text-Rank, and KM-MMR. In conclusion, the proposed method in this paper can obtain better-quality text summaries.

Affiliated Institutions

• Xihua University CN

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