Review of Techniques for Automatic Text Summarization

  • B. Shiva Prakash
  • K. V. Sanjeev
  • Ramesh Prakash
  • K. ChandrasekaranEmail author
  • M. V. Rathnamma
  • V. Venkata Ramana
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1090)


Summarization refers to the process of reducing the textual components such as words and sentences but conveying most of the information in the input text. Research in summarization is very prominent in the current scenario where the textual data available is enormous and contains valuable information. People have been interested in summarization since time immemorial. The methods adopted in the past relied on manually reading the text and based on one’s understanding of the text, manually generating the summary. In the current world, due to the explosion of data from Internet and social media, the manual process is very tedious and time-consuming. As a result, there is a great need to automate the process of summarization. In this paper, we summarize most of the researches in the field of summarization which is unique and path-breaking.


Automatic text summarization Extraction-based summarization Abstraction-based summarization 


  1. 1.
    Torres-Moreno, Juan-Manuel. 2014. Automatic Text Summarization. Wiley.Google Scholar
  2. 2.
    Fattah, Mohamed Abdel, and Fuji Ren. 2008 Automatic Text Summarization. World Academy of Science, Engineering and Technology 37 (2008).Google Scholar
  3. 3.
    Das, Dipanjan, and André F.T. Martins. 2007. A Survey on Automatic Text Summarization. Literature Survey for the Language and Statistics II course at CMU 4: 192–195.Google Scholar
  4. 4.
    Church, Kenneth, and William Gale. 1999. Inverse Document Frequency (idf): A Measure of Deviations from Poisson. Natural Language Processing Using Very Large Corpora, 283–295. Netherlands: Springer.CrossRefGoogle Scholar
  5. 5.
    RamakrishnaMurty, M., J.V.R Murthy, P.V.G.D. Prasad Reddy, and Suresh. C. Sapathy. 2012. A Survey of Cross-Domain Text Categorization Techniques. In International conference on Recent Advances in Information Technology RAIT-2012, ISM-Dhanabad, IEEE Xplorer Proceedings. 978–1-4577-0697-4/12.Google Scholar
  6. 6.
    Mani, Inderjeet. 2001. Automatic Summarization, vol. 3. John Benjamins Publishing.Google Scholar
  7. 7.
    Erkan, Günes, and Dragomir R. Radev. 2004. LexRank: Graph-based Lexical Centrality as Salience in Text Summarization. Journal of Artificial Intelligence Research 22: 457–479.CrossRefGoogle Scholar
  8. 8.
    Mihalcea, Rada. 2004. Graph-Based Ranking Algorithms for Sentence Extraction, Applied to Text Summarization. In ACL 2004 on Interactive Poster and Demonstration Sessions. Association for Computational Linguistics.Google Scholar
  9. 9.
    Carbonell, Jaime, and Jade Goldstein. 1998. The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries. In 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM.Google Scholar
  10. 10.
    Radev, Dragomir R., Hongyan Jing, and Malgorzata Budzikowska. 2000. Centroid-Based Summarization of Multiple Documents: Sentence Extraction, Utility-Based Evaluation, and User Studies. In NAACL-ANLP Workshop on Automatic summarization. Association for Computational Linguistics.Google Scholar
  11. 11.
    Page, Lawrence, et al. 1990. The PageRank Citation Ranking: Bringing Order to the Web.Google Scholar
  12. 12.
    Lin, Chin-Yew, and Eduard Hovy. 2002. Manual and Automatic Evaluation of Summaries. In ACL-02 Workshop on Automatic Summarization-Volume 4. Association for Computational Linguistics.Google Scholar
  13. 13.
    Litvak, Marina, and Natalia Vanetik. 2014. Multi-document Summarization Using Tensor Decomposition. Computación y Sistemas 18 (3): 581–589.CrossRefGoogle Scholar
  14. 14.
    Marcu, Daniel. 1998. Improving Summarization Through Rhetorical Parsing Tuning. In The 6th Workshop on Very Large Corpora.Google Scholar
  15. 15.
    Robertson, Stephen. 2004. Understanding Inverse Document Frequency: On Theoretical Arguments for IDF. Journal of documentation 60 (5): 503–520.CrossRefGoogle Scholar
  16. 16.
    Banko, Michele, Vibhu O. Mittal, and Michael J. Witbrock. 2000. Headline Generation Based on Statistical Translation. In Proceedings of the 38th Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics.Google Scholar
  17. 17.
    Hovy, Eduard, and Chin-Yew Lin. 1998. Automated Text Summarization and the SUMMARIST System. In Proceedings of a workshop on held at Baltimore, Maryland: October 13–15, 1998. Association for Computational Linguistics.Google Scholar
  18. 18.
    Knight, Kevin, and Daniel Marcu. 2000. Statistics-Based Summarization-step One: Sentence Compression. AAAI/IAAI 2000: 703–710.Google Scholar
  19. 19.
    Torres-Moreno, Juan-Manuel. 2012. Artex is Another Text Summarizer. arXiv preprint arXiv:1210.3312.
  20. 20.
    Barzilay, Regina, and Michael Elhadad. 1999. Using Lexical Chains for text Summarization. Advances in Automatic Text Summarization 111–121.Google Scholar
  21. 21.
    Fernandez, Silvia, Eric SanJuan, and Juan Manuel Torres-Moreno. 2007. Textual Energy of Associative Memories: Performant Applications of Enertex Algorithm in Text Summarization and Topic Segmentation. In Mexican International Conference on Artificial Intelligence. Springer, Berlin Heidelberg.Google Scholar
  22. 22.
    Svore, Krysta Marie, Lucy Vanderwende, and Christopher JC Burges. 2007. Enhancing Single-Document Summarization by Combining RankNet and Third-Party Sources.” EMNLP-CoNLL.Google Scholar
  23. 23.
    Saggion, Horacio. 2008. A Robust and Adaptable Summarization Tool. Traitement Automatique des Langues 49 (2).Google Scholar
  24. 24.
    Filippova, Katja. 2010. Multi-Sentence Compression: Finding Shortest Paths in Word Graphs. In Proceedings of the 23rd International Conference on Computational Linguistics. Association for Computational Linguistics. Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • B. Shiva Prakash
    • 1
  • K. V. Sanjeev
    • 1
  • Ramesh Prakash
    • 1
  • K. Chandrasekaran
    • 1
    Email author
  • M. V. Rathnamma
    • 2
  • V. Venkata Ramana
    • 3
  1. 1.Department of Computer Science and EngineeringNational Institute of Technology Karnataka SurathkalMangaloreIndia
  2. 2.Kandula Srinivasa Reddy Memorial College of EngineeringKadapaIndia
  3. 3.Chaitanya Bharathi Institute of TechnologyProddaturIndia

Personalised recommendations