Text Summarization: An Extractive Approach

  • Vishal Soni
  • Lokesh Kumar
  • Aman Kumar Singh
  • Mukesh KumarEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1154)


Text summarization method produces the shorter or abstract version of text after giving the large source text. It provides the meaningful information of the source text, i.e. the text’s meaning is intact and accurate. Text summarization tools have a powerful impact on today’s world due to the increasing information with a massive rate on the Internet. It is very difficult for a person to describe and ingest the whole content. The manual conversion or summarization is very difficult task, hence automation is need. The automation can be achieve using artificial intelligence techniques. Text summarization methods are classified into two categories: Extractive and abstractive. The extractive method, as its name suggests, consists of extracting important sentences or paragraph from some source of text and rejoining them to get the summarized form of the source content. The criteria for evaluating an importance of a sentence or paragraph is based on the statistical features parameter of the sentences, and the abstractive method is all about knowing the source text and re-writing the text in a few words that describes the whole source text. In addition, this method uses a linguistic approach to check and interpret the source text. In this article, extractive text summarization methods are applied to the job. The validation of the model is performed using the bench-marked source text. From the obtained result, it is evident that the summarization model performs well and do the summarization which is very precise and meaningful.


Text summarization Extractive Abstractive TF-IDF 


  1. 1.
    Gupta, V., Lehal, G.S.: A survey of text summarization extractive techniques. J. Emerg. Technol. Web Intell. 2(3), 258–268 (2010)Google Scholar
  2. 2.
    Das, D., Martins, A.F.T.: A survey on automatic text summarization. Liter. Surv. Lang. Stat. II Course at CMU 4(192–195), 57 (2007)Google Scholar
  3. 3.
    Sabharwal, M.: The use of soft computing technique of decision tree in selection of appropriate statistical test for hypothesis testing. In: Soft Computing: Theories and Applications, pp. 161–169. Springer (2018)Google Scholar
  4. 4.
    Motwani, K.R., Jitkar, B.D.: A model framework for enhancing document clustering through side information. In: Soft Computing: Theories and Applications, pp. 195–207. Springer (2018)Google Scholar
  5. 5.
    Mohd, M., Hashmy, R.: Question classification using a knowledge-based semantic kernel. In: Soft Computing: Theories and Applications, pp. 599–606. Springer (2018)Google Scholar
  6. 6.
    Mahajan, R.: Emotion recognition via EEG using neural network classifier. In: Soft Computing: Theories and Applications, pp. 429–438. Springer (2018)Google Scholar
  7. 7.
    Villa-Monte, A., Lanzarini, L., Bariviera, A.F., Olivas, J.A.: User-oriented summaries using a PSO based scoring optimization method. Entropy 21(6), 617 (2019)Google Scholar
  8. 8.
    Qazvinian, V., Radev, D.R.: Scientific paper summarization using citation summary networks. In: Proceedings of the 22nd International Conference on Computational Linguistics, vol. 1, pp. 689–696. Association for Computational Linguistics (2008)Google Scholar
  9. 9.
    Saggion, H., Lapalme, G.: Concept identification and presentation in the context of technical text summarization. In: NAACL-ANLP 2000 Workshop: Automatic Summarization (2000)Google Scholar
  10. 10.
    Allahyari, M., Pouriyeh, S., Assefi, M., Safaei, S., Trippe, E.D., Gutierrez, J.B., Kochut, K.: Text summarization techniques: a brief survey. arXiv:1707.02268 (2017)
  11. 11.
    Wu, P., Zhou, Q., Lei, Z., Qiu, W., Li, X.: Template oriented text summarization via knowledge graph. In: 2018 International Conference on Audio, Language and Image Processing (ICALIP), pp. 79–83. IEEE (2018)Google Scholar
  12. 12.
    Cepi Slamet, A.R., Atmadja, D.S., Maylawati, R.S., Lestari, W.D., Ali Ramdhani, M.: Automated text summarization for Indonesian article using vector space model. In: IOP Conference Series: Materials Science and Engineering, vol. 288, p. 012037. IOP Publishing (2018)Google Scholar
  13. 13.
    Ramachandran, L., Cheng, J., Foltz, P.: Identifying patterns for short answer scoring using graph-based lexico-semantic text matching. In: Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications, pp. 97–106 (2015)Google Scholar
  14. 14.
    Khan, R., Qian, Y., Naeem, S.: Extractive based text summarization using k-means and tf-idf (2019)Google Scholar
  15. 15.
    Langville, A.N., Meyer, C.D., FernÁndez, P.: Google’s pagerank and beyond: the science of search engine rankings. Math. Intell. 30(1), 68–69 (2008)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Vishal Soni
    • 1
  • Lokesh Kumar
    • 1
  • Aman Kumar Singh
    • 1
  • Mukesh Kumar
    • 1
    Email author
  1. 1.Department of CSENIT PatnaPatnaIndia

Personalised recommendations