An Intelligent Automatic Text Summarizer

  • M. Shoaib Jameel
  • Anubhav 
  • Nilesh Singh
  • Nitin Kumar Singh
  • Chingtham Tejbanta Singh
  • M. K. Ghose
Conference paper


This paper describes an intelligent text summarizer that summarizes a given piece of text into three different summaries based on three different algorithms. This summarizer uses statistical methods to summarize a text like considering the frequency of words, rare words etc. It then gives a meaningful title to the main text and finally selects the best summary out of a list of given summaries. This summarizer allots the writer a competence level (in written English) after analyzing the text like number of rare words used. The title generator of the summarizer gives a short title to the main text. Results obtained through experiments showed that it is indeed possible to determine the competence level of the writer from the text and proximity of the sentences play a vital role in selecting the best summary.


Selection Algorithm Main Text Proper Noun Competence Level Good Summary 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Eduard H., Text Summarization. Chapter 32.Google Scholar
  2. 2.
    Berger A.L., Mittal V.O.: OCELOT: A System for Summarizing Web Pages, Proceeding of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, p. 144–151, July 24–28, 2000, Athens, Greece (2000)Google Scholar
  3. 3.
    Buyukkokten O., Garcia-Molina H., Paepeke A. Seeing the while in parts: Text Summarization for Web Browsing on Handheld Devices, Proceedings of the 10th International Conference on World Wide Web, p. 652–662, May 01–05, 2001, Hong Kong (2001)Google Scholar
  4. 4.
    Hu, Y., Xin, G., Song, R, Hu, G et al.: Title Extraction from Bodies of HTML Documents and its Application to Web Page Retrieval. Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (2005)Google Scholar
  5. 5.
    Xue Y, Hu Y., Xin, G., Song, R., Shi S., et al.: Web Page Title Extraction and its application, Information Processing and Management: An International Journal. 43, 1332–1347, September, 2007.CrossRefGoogle Scholar
  6. 6.
    Shoaib J.M. et al.: Enhancements in Query Evaluation and Page Summarization of The Thinking Algorithm. In the Proceedings of the Third International Symposium on Information Technology, Kuala Lumpur, Malaysia, vol. III, pp. 1979–1987Google Scholar
  7. 7.
    Lin C-Y, Hovy E.: Identify Topics by Position. Proceedings of the 5th Conference on Applied Natural Language Processing, March (1997).Google Scholar
  8. 8.
    Dalianis H., Hovy E.: Aggregation in Natural Language Generation: an Artificial Intelligence Perspective, EWNLG’93, Fourth European Workshop, Lecture Notes in Artificial Intelligence, No. 1036, pp. 88–105, Springer Verlag (1996)Google Scholar
  9. 9.
    Dalianis H.: ASTROGEN — Aggregated deep and Surface naTuRal language GENerator [Online] Available:≈hercules/ASTROGEN/ASTROGEN.html (1999)Google Scholar
  10. 10.
    Marcu D.: From Discourse Structures to Text Summaries. The Proceedings of the ACL’97/EACL’97 Workshop on Intelligent Scalable Text Summarization, pp 82–88, Madrid, Spain., July (1997)Google Scholar
  11. 11.
    McKeown K., Radev D.: Generating summaries of multiple news articles. In Proceedings, 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 74–82, Seattle, Washington, July (1995)Google Scholar
  12. 12.
    Luhn, H.P., Automatic Creation of Literature Abstracts. IBM Journal (1958), 159–165.Google Scholar
  13. 13.
    Edmundson, H.P., New Methods in Automatic Extracting, Journal of the ACM 16(2): 264–285 (1958)CrossRefGoogle Scholar
  14. 14.
    Hassel, M., Dalianis H.: Generation of Reference Summaries. In the proceedings of the 2nd Language & Technology Conference: Human Language Technologies as a Challange for Computer Science and Linguistics, April 21–23 2005, Poznan, Poland.Google Scholar
  15. 15.
    Chuang, W.T., Yang, J.: Text Summarization by Sentences Segment Extraction Using Machine Learning Algorithms. Springer Berlin/Herdelberg 454–457. July 13, (2007)Google Scholar
  16. 16.
    Wang, J., Zhou, S., Hu, Yun-Fa.: Sentence Clustering based automatic Summarization. Machine Learning and Cybernetics, 2003 International Conference on Digital Object Identifier 1, 57–62 (2003).Google Scholar
  17. 17.
    Mitra, M., Singhal, A., Buckley, C.: Automatic Text Summarization by Paragraph Extraction. In Proceedings of the ACL’ 97/EACL’ 97 Workshop on Intelligent Scalable Text Summarization (Madrid, Spain, 1997), pp 31–36 (1997)Google Scholar

Copyright information

© Indian Institute of Information Technology, India 2009

Authors and Affiliations

  • M. Shoaib Jameel
    • 1
  • Anubhav 
    • 1
  • Nilesh Singh
    • 1
  • Nitin Kumar Singh
    • 1
  • Chingtham Tejbanta Singh
    • 1
  • M. K. Ghose
    • 1
  1. 1.Dept. of Computer Science and EngineeringSikkim Manipal Institute of TechnologyEast SikkimIndia

Personalised recommendations