A Frequency-Driven Approach for Extractive Text Summarization


Ashwini Zadgaonkar


Due to Digital Revolution, most books and newspaper articles are now available online. Particularly for kids and students, prolonged screen time might be bad for eyesight and attention span. As a result, summarizing algorithms are required to provide long web content in an easily digestible style. The proposed methodology is using term frequency and inverse document frequency driven model, in which the document summary is generated based on each word in a corpus. According to the preferred method, each sentence is rated according to its tf-idf score, and the document summary is produced in a fixed ratio to the original text. Expert summaries from
a data set are used for measuring precision and recall using the proposed approach’s ROUGE model. towards the development of such a framework is presented.


How to Cite
Ashwini Zadgaonkar. (2023). A Frequency-Driven Approach for Extractive Text Summarization. International Journal of Next-Generation Computing, 14(1). https://doi.org/10.47164/ijngc.v14i1.1019


  1. Agrawal s, T. s. 2014. Video lecture summarization system. International journal of Computer Applications,..
  2. Allahyari M, Pouriyeh S, A. M. S. S. T. E. G. J. K. K. 2017. Text summarization techniques: a brief survey. International Journal of Advanced Computer Science and Applications (IJACSA).. DOI: https://doi.org/10.14569/IJACSA.2017.081052
  3. BHARAT, K. and HENZINGER, M. R. 1998. Improved algorithms for topic distillation in a hyperlinked environment. DOI: https://doi.org/10.1145/290941.290972
  4. Christian, Hans Agus, M. . S. D. 2016. Single document automatic text summarization using term frequency-inverse document frequency (tf-idf). ComTech: Computer, Mathematics and Engineering Applications.. DOI: https://doi.org/10.21512/comtech.v7i4.3746
  5. Gupta, V. 2013. A survey of text summarizers for indian languages and comparison of their performance. Journal of Emerging Technologies in Web Intelligence. DOI: https://doi.org/10.4304/jetwi.5.4.361-366
  6. Horacio Saggion, T. P. 2012. text summarization: Past, present, and future. Multi-source, multilingual information extraction and summarization. DOI: https://doi.org/10.1007/978-3-642-28569-1_1
  7. Jafari, Mehdi Wang, J. . Q. Y. . G. M. . S. A. . T. X. 2016. Automatic text summarization using fuzzy inference. DOI: https://doi.org/10.1109/IConAC.2016.7604928
  8. Kabeer, R. and Idicula., S. M. 2014. Text summarization for malayalam documents — an experience. DOI: https://doi.org/10.1109/ICDSE.2014.6974627
  9. Kamble, Satish Mandage, S. . T. S. . V. D. . B. P. 2017. Survey on summarization techniques and existing work. International Journal of Applied Engineering Research Vol.12, No.1.
  10. Kumar, Sreedhi C, S. . G. A. 2018. Semantic representation of texts in indian languages — a review.
  11. Munot, N., G. S. 2022. Comparative study of text summarization methods. International Journal of Next-Generation Computing. 13, 5.
  12. Ramesh Chandra Belwal, Sawan Rai1, A. G. 2020. A new graph-based extractive text summarization using keywords or topic modeling. Springer-Verlag GmbH Germany, part of Springer Nature 202, .
  13. Salton, G. and Buckley, C. 1988. Term-weighting approaches in automatic text retrieval. Information Processing Management,.. DOI: https://doi.org/10.1016/0306-4573(88)90021-0
  14. Vetriselvi T, G. N. 2021. An improved key term weightage algorithm for text summarization using local context information and fuzzy graph se DOI: https://doi.org/10.1007/s12652-022-04169-1