Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
YongdoKim, “Text binds Theory”. Pusan University of Foreign Studies Press. 1996
Harabagiu, S. Finley L. “Topic Themes for Multi Document Summarization,” In proceeding of ACM SIGIR, 202-209, 2005.
Sakurai, T., Utsumi, A. “Query-based Multi document Summarization for Information Retrieval,” The Proceeding of NTCIR, 2004.
Goldstein. J., Mittal. V. Carbonell. J., Callan. J.,”Creating and Evaluating Multi-Document Sentence Extract Summaries,” The Proceeding of CIKM, 165-172, 2000.
Inderjeet Mani, Automatic Summarization, Kohn Benjamins publishing Co., 2001.
Xiaojun Wan, Jianwu Yang “Multi-Doucument Summarization Using Cluster-based Ling Analysis”. Proceedings of the International Conference SiGIR’08, 2008. PP. 299-306
Lewis, D.D, Sparck Jones, K. “Natural language processing for information retrieval,” Communications of the ACM, Vol. 39, No.1, 1996, pp. 92-101
Liddy, E.D, Myaeng, S.H. “DR-LINK’s: linguistic-comceptual approach to document and detection,”,” The First Text The First Text REtreival Conference (TREC-1), 1993, pp. 113-129.
Morris, J, Hirst, G. “Lexical cohesion computed by thesaural relations as an indicator of the structure of text,” Computational Linguistics, Vol.17, No.1, pp. 21-43, 1991.
JunSeok Cha, Seunghyeon Bak, PanKoo Kim. “An Approach of Sentence Segmentation for Improving Parsing Effecieny” Korea MultimediA Society Vol. 19, No. 1, 2016. Pp.63-66
C.Y.Lin, E.H.Hovy, “Automatic evaluation of summaries using n-gram co-occurrence statistics”, In Proceedings of Human Language Technology Conference (HLT-NAACL 2003), Edmonton, Canada, May, 2004.
Young-In Song, Kyoung-Soo Han, Hae-Chang Rim. “Automatic Summarization based on Lexical Chains considering Word Assocication.”, The 14th Annual Conference on Human & Cognitive Language Technology 2002. pp. 300-305
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Cha, J., Kim, P.K. (2017). The Automatic Text Summarization Using Semantic Relevance And Hierarchical Structure Of Wordnet. In: Barolli, L., Xhafa, F., Yim, K. (eds) Advances on Broad-Band Wireless Computing, Communication and Applications. BWCCA 2016. Lecture Notes on Data Engineering and Communications Technologies, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-319-49106-6_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-49106-6_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-49105-9
Online ISBN: 978-3-319-49106-6
eBook Packages: EngineeringEngineering (R0)