Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (10): 44-50.doi: 10.11896/jsjkx.200900082

• Artificial Intelligence • Previous Articles     Next Articles

Graph Based Collaborative Extraction Method for Keywords and Summary from Documents

MAO Xiang-ke1,2,3, HUANG Shao-bin1, YU Qin-yong2,3   

  1. 1 College of Computer Science and Technology,Harbin Engineering University,Harbin 150001,China
    2 CETC Big Data Research Institute Co.,Ltd.,Guiyang 550022,China
    3 Big Data Application on Improving Governance Capabilities National Engineering Laboratory,Guiyang 550022,China
  • Received:2020-09-10 Revised:2021-03-10 Online:2021-10-15 Published:2021-10-18
  • About author:MAO Xiang-ke,born in 1992,Ph.D.His main research interests include natural language processing and machine lear-ning.
    HUANG Shao-bin,born in 1965,professor.His main research interests include data mining,natural language proces-sing and machine learning.
  • Supported by:
    Big Data Application on Improving Governance Capabilities National Engineering Laboratory Open Fund Project.

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Abstract: The purpose of keywords extraction and summary extraction is to select key content from the original document to express the main meaning of the original document.The evaluation of keywords and summarization quality mainly depends on whether it can cover the main topics of the document.In the existing methods of keywords extraction and summary extraction based on graph models,it rarely involves the task of keywords extraction and summary extraction collaboratively.The article proposes a method based on a graph model for simultaneous keywords extraction and summary extraction.The method first uses the six relationships among words,topics,and sentences in the document,including words-words,topics-topics,sentences-sentences,words-topics,topics-sentences,words-sentences,to construct the graph;then uses the statistical characteristics of the words and sentences in the document to evaluate the prior importance of each vertex in the graph;next,it uses an iterative way to score words and sentences;finally,we get the final keywords and summary based on the scores of words and sentences.In order to verify the effectiveness of the proposed method,keywords extraction and summary extraction experiments are carried out on Chinese and English datasets.It is found that the proposed method achievs good results in both keywords extraction and summary extraction tasks.

Key words: Extractive summarization, Graph model, Keywords extraction, Topic cover

CLC Number: 

  • TP311.131
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