Mongolian Automatic Text Summarization Method Based on Pre-trained Model and Improved TextRank


Yongshun Han, Qintu Si*, Siriguleng Wang

College of Computer Science and Technology, Inner Mongolia Normal University, Hohhot, Inner Mongolia, China.

*Corresponding author: Qintu Si

Published: May 16,2024


At present, there is limited research on automatic Mongolian text summarization, especially using mainstream methods. The existing TextRank algorithm only considers the similarity between sentences, ignoring the characteristics of the sentence itself. In this paper, a Mongolian automatic text summarization method called IMNUBERT-mnTextRank, based on a pre-trained model and an enhanced TextRank algorithm, is proposed. The information from the Mongolian external knowledge base is incorporated into the TextRank algorithm in the form of sentence vectors to enhance the accuracy of similarity calculations between sentences. The process of calculating sentence weights is optimized by considering sentence features such as sentence position, similarity to the title, keyword coverage rate, and Mongolian conjunctions. Finally, the weight of each sentence is obtained through algorithm iteration. After sorting the sentences, the top two are selected for the summary. Experimental results show that, compared with the TextRank algorithm, the Rouge-1, Rouge-2, and Rouge-L indicators of the proposed method have improved by 0.183, 0.179, and 0.199, respectively. Consequently, the quality of the generated Mongolian summarization is enhanced.


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How to cite this paper

Mongolian Automatic Text Summarization Method Based on Pre-trained Model and Improved TextRank

How to cite this paper: Yongshun Han, Qintu Si, Siriguleng Wang. (2024) Mongolian Automatic Text Summarization Method Based on Pre-trained Model and Improved TextRank. Advances in Computer and Communication5(2), 141-147.