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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 1
129-133

Research on automatic text summarization combining topic feature

Luo Fang1
Wang Jinghang1
He Daosen2
Pu Qiumei3
1. School of Computer Science & Technology, Wuhan University of Technology, Wuhan 430063, China
2. Dept. of Supply Chain & Information Management, Hang Seng University of Hong Kong, Hong Kong 999077, China
3. School of Information Engineering, Minzu University of China, Beijing 100081, China
DOI: 10.19734/j.issn.1001-3695.2019.09.0590

Abstract

Aiming at the traditional graph models for text summarization only focus on statistical features or shallow semantic features, and lack mining and utilization of deep topic semantic features, this paper proposed MDSR(multi-dimension summarization rank), an automatic text summarization method that combined topic feature. Specifically, this method adopted the LDA model to mine the semantic information of text topics and measured the impact of topic feature on a sentence by defining the importance of the topic. And it improved the construction mode of the probability transition matrix of graph model nodes by combining the topic feature with statistic features and inter-sentence similarity. Finally, it extracted and measured summarization according to the weight of sentence nodes. The results show that the ROUGE value evaluates by MDSR reaches the best when the weight ratio of topic feature, statistic feature and inter-sentence similarity is 3: 4: 3. The ROUGE-1, ROUGE-2, ROUGE-SU4 are 53.35%, 35.18% and 33.86%, which perform better than other comparisons. It shows that the text summarization method combining topic feature can effectively improve the accuracy of the summarization extraction.

Foundation Support

国家教育部人文社会科学研究规划基金资助项目(18YJAZH087)
武汉理工大学自主创新研究基金资助项目(3120600100)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.09.0590
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 1
Section: Algorithm Research & Explore
Pages: 129-133
Serial Number: 1001-3695(2021)01-026-0129-05

Publish History

[2021-01-05] Printed Article

Cite This Article

罗芳, 汪竞航, 何道森, 等. 融合主题特征的文本自动摘要方法研究 [J]. 计算机应用研究, 2021, 38 (1): 129-133. (Luo Fang, Wang Jinghang, He Daosen, et al. Research on automatic text summarization combining topic feature [J]. Application Research of Computers, 2021, 38 (1): 129-133. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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