The Automatic Text Summarization Using Semantic Relevance And Hierarchical Structure Of Wordnet

  • JunSeok ChaEmail author
  • Pan Koo Kim
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 2)


In recent years, rapid development and spread of smart devices has resulted in increase in data of online documents on the Internet day by day. Growing information overload of web texts leaves users facing difficulties in browsing and understanding huge data in web pages. Therefore, in the field of automatic document summarization, diverse studies are underway to find ways of creating summaries efficiently. This study aims to propose document summarization methods using sentence segmentation and lexical chaining to extract important sentences of a given text and make a summary by excluding unnecessary sentences. Sentences of a given text are divided by analyzing their syntactic structure or identifying parts of speech of words and phrases and clauses used in the sentences. Important sentences are extracted by means of lexical chain. Results of previous document summarization research were improved through experiment, allowing for a summary using key points of a text.


Natural Language Processing Split sentence Automatic Text Summarization lexical chain 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Dept of Software Convergence EngineeringChosun UniversityGwangjuKorea

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