Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (11A): 231200005-10.doi: 10.11896/jsjkx.231200005

• Intelligent Computing • Previous Articles     Next Articles

Deep Learning-based Method for Mining Ocean Hot Spot News

QIN Xianping1, DING Zhaoxu1, ZHONG Guoqiang1, WANG Dong2   

  1. 1 College of Computer Science and Technology,Ocean University of China,Qingdao,Shandong 266404,China
    2 Library of Ocean University of China,Qingdao,Shandong 266404,China
  • Online:2024-11-16 Published:2024-11-13
  • About author:QIN Xianping,born in 2000,postgra-duate.Her main research interests include neural architecture search and natural language processing.
    WANG Dong,born in 1979,Ph.D,senior engineer.His main research interests include machine vision, embedded system,software programming, and IoT design.
  • Supported by:
    Scientific and Technological Innovation 2030－Major Project for New Generation of AI(2018AAA0100400),Natural Science Foundation of Shandong Province,China(ZR2020MF131,ZR2021ZD19),Science and Technology Program of Qingdao(21-1-4-ny-19-nsh) and Fundamental Research Funds for the Central Universities(202253006).

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Abstract: The rapid development of the mobile Internet and the popularity of modern mobile clients promote the vigorous deve-lopment of the online news industry,social media and self-media,etc.,providing users with diverse and rich information.With the steady advancement of China's maritime power strategy and the significant enhancement of national maritime eawareness,the Internet is flooded with multifaceted information on the ocean field, with relevant media reports and public opinions proliferating online and hotspot events occurring frequently.Aiming at multi-source and multi-attribute network marine information,based on multi-source text clustering and automatic summarization technology,an automatic deep learning-based ocean hot news mining system is proposed,including five functional modules:automatic collection of multi-source ocean-related data,data preprocessing,feature extraction,text clustering,and automatic summarization.Specifically,the web crawler program collects diverse and scattered ocean data from multiple data sources,automatically structures the data and stores it in the database;clustering analysis is performed based on the similarity of text features and relationships between texts,which provides data support for subsequent summarization generation and topic discovery.Additionally,an automatic summary generation method for ocean news is proposed,leveraging the powerful contextual understanding and rich language expression abilities of the pre-trained language mo-dels.Multiple experiments demonstrate the effectiveness of the proposed method in each evaluation index,highlighting its superiority in mining news on multi-source heterogeneous networks.This method provides a feasible solution for processing scattered marine information and generating more readable content summaries,significantly contributing to the enhancement of marine information retrieval efficiency,monitoring public opinion trends,and promoting the application and dissemination of marine information.

Key words: Ocean news, Text clustering, Automatic summarization, Deep learning, Natural language processing, Pre-trained model

CLC Number: 

  • TP391
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