Text Summarization Using Word Frequencies

[1] Research Article An Automatic Multi document Text Summarization Approach Based on Naive Bayesian Classifier Using Timestamp Strategy.
[2] Research Article An Automatic Multi document Text Summarization Approach Based on Naive Bayesian Classifier Using Timestamp St.
[3] C. Lakshmi Devasena and M. Hemalatha, "Automatic Text categorization and summarization using rule reduction," IEEE-International
Conference On Advances in Engineering, Science And Management (ICAESM -2012), Nagapattinam, Tamil Nadu, 2012, pp. 594-598.
[4] J. N. Madhuri and R. Ganesh Kumar, "Extractive Text Summarization Using Sentence Ranking," 2019 International Conference on Data Science and Communication (IconDSC), Bangalore, India, 2019, pp. 1-3.
[5] A. R. Pal and D. Saha, "An approach to automatic text summarization using WordNet," 2014 IEEE International Advance Computing Conference(IACC),Gurgaon, 2014, pp. 1169-1173.
[6] D. Hingu, D. Shah and S. S. Udmale, "Automatic textsummarization of Wikipedia articles," 2015 International Conference on Communication,Information & Computing Technology (ICCICT), Mumbai, 2015, pp. 1-4.
[7] Steinberger, J., & Ježek, K. (2008). Automatic Text Summarization (The state of the art 2007 and new challenges). Znalosti, 30(2), 1-12.
[8] Yohei, S. (2002). Sentence extraction by TF/IDF and Position Weighting from newspaper articles. In Proceedings of the Third NTCIR Workshop.
[9] Das, D., & Martins, A. F. (2007). A survey on automatic text summarization. Literature Survey for the Language and Statistics, 3(3), 1-12.
[10]Santosh Kumar Bharti, “Automatic Keyword Extraction for Text Summarization in Multidocument eNewspapers
Articles”, EuropeanJournal of Advances in Engineering and Technology, Vol. 4, Issue 6, pp. 410-427, 2017.
[11]Roshna Chettri, Udit Kr. Chakraborty, “Automatic Text Summarization”, International Journal of Computer Applications, Vol. 161, Issue 1, pp. 5-7, 2017.