Automatic Summary Generation Method Based on Multidimensional Text Feature

• Published in: Jisuanji gongcheng
• Main Author: WANG Qingsong, ZHANG Heng, LI Fei
• Format: Article
• Language: English
• Published: Editorial Office of Computer Engineering 2020-09-01
• Subjects: sentence similarity|graph integration model|text summarization|naive bayes|multidimensional feature
• Online Access: https://www.ecice06.com/fileup/1000-3428/PDF/20200914.pdf

Existing automatic summary generation methods for long texts cannot fully measure the similarity characteristics of sentences,which results in the decrease of the accuracy of summary generation.To address the problem,this paper proposes an automatic summary generation method based on graph integration model.The method calculates the word frequency,semantic features and syntactic features of text sentences,and then uses the naive Bayesian method to transform the fusion problem of multidimensional text features into a graph integration mode,which improves the calculation accuracy of similarity between sentences.On this basis,a text summary is generated by using TextRank algorithm.Experimental results show that compared with the traditional summary generation method based on the sequence-to-sequence model and summary extraction method based on multi-dimensional features of sentences,the proposed method achieves a higher ROUGE index.It can effectively synthesize the multidimensional features of sentences,and improves the accuracy of summary generation.