Text Summarizer Using NLP (Natural Language Processing)

References

[1] Adhika Widyassari, S. R. (2020). Review of automatic text summarization techniques & methods. Journal of King Saud University - Computer and Information Sciences, 18.

[2] Amigó E, G. J. (2005). a framework for the evaluation of text summarization systems. proceedings of the 43rd annual meeting on association for computational linguistics. ACL ’05.

[3] Antiqueira L, O. O. (2007). A complex network approach to text summarization. Information Sciences.

[4] B. Cretu, Z. C. (2002). Automatic summarization based on sentence extraction. International Journal of Applied Electromagnetic and mechanics.

[5] Brownlee, J. (2019, August 7). A Gentle Introduction to Text Summarization. Retrieved from https://machinelearningmastery.com/: https://machinelearningmastery.com/gentle-introduction-text-summarization/

[6] Changjian Fanga, D. M. (2016, March 5). Word-sentence co-ranking for automatic extractive text summarization. Retrieved from https://www.sciencedirect.com/: https://www.sciencedirect.com/science/article/abs/pii/S0957417416306959?via%3Dihub

[7] Conroy, J. M. (2001). Text summarization via hidden markov models. Proceedings of SIGIR '01.

[8] D. Gillick, K. R. (2009). A global optimization network for meeting summarization. Proc. IEEE Int. Conf. Acoust, 1-4.

[9] Darji, H. (2020, January 8). Text Summarization-Key Concepts. Retrieved from https://medium.com/: https://medium.com/@harshdarji_15896/text-summarization-key-concepts-23df617bfb3e

[10] Evans, D. K. (2005). Similarity-based multilingual multidocument summarization. Technical Report CUCS-014-05.

[11] Gupta V, L. G. (2010). A survey of text summarization extractive techniques. J Emerg Technol Web Intell, 258-268.

[12] J. Patel, P. (2015). https://machinelearningmastery.com/gentle-introduction-text-summarization/. International Journal of Engineering and Computer Science, 5.

[13] Jain, A. (2019, April 1). Automatic Extractive Text Summarization using TF-IDF. Retrieved from Medium.com: https://medium.com/voice-tech-podcast/automatic-extractive-text-summarization-using-tfidf-3fc9a7b26f5

[14] KS, J. (2007). Automatic summarising: the state of the art. Inf Process Manag 43, 1449-1487.

[15] Kumar, T. (2014). Automatic Text Summarization. Rourkela.

[16] Mayo, M. (2019, November). Getting Started with Automated Text Summarization. Retrieved from https://www.kdnuggets.com/: https://www.kdnuggets.com/2019/11/getting-started-automated-text-summarization.html

[17] Mr. Vikrant Gupta, M. P. (2012). An Statiscal Tool for Multi-Document Summarization. International Journal of Scientific and Research (ISSN 2250-3153).

[18] Neelima Bhatia, A. J. (2015). Literature Review on Automatic Text Summarization: Single and Multiple Summarizations. International Journal of Computer Applications, 1-5.

[19] Okumura, H. T. (2009). Text Summarization Model based on the budgeted median problem. Proc. 18th ACM Conf. Inf. Knowledge, 1-4.

[20] Opidi, A. (2019, April 15). A Gentle Introduction to Text Summarization in Machine Learning. Retrieved from https://blog.floydhub.com/: https://blog.floydhub.com/gentle-introduction-to-text-summarization-in-machine-learning/

[21] Panchal, A. (2019, June 10). NLP — Text Summarization using NLTK: TF-IDF Algorithm. Retrieved from https://towardsdatascience.com/: https://towardsdatascience.com/text-summarization-using-tf-idf-e64a0644ace3

[22] Recent automatic text summarization techniques: a survey. (2019, March 29). Retrieved from https://link.springer.com/: https://link.springer.com/article/10.1007/s10462-016-9475-9

[23] S, S. (2011). Automatic Text Summarization: The current state of the art. International Journal of.

[24] YLLIAS CHALI, S. A. (2011). Query-focused multi-document summarization: automatic data annotations and supervised learning approaches. Cambirdge University Press.

@article{1703633,
      author = {Aakash Srivastava, Himanshu Daharwal, Kamal Chauhan, Nikhil Mukati, Pranoti Shrikant Kavimandan},
      title = {Text Summarizer Using NLP (Natural Language Processing)},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {6},
      number = {1},
      pages = {211-216},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1703633.pdf},
      abstract = {Enormous amounts of information are available online on the World Wide Web. To access information from databases, search engines like Google and Yahoo were created. Because the amount of electronic information is growing every day, the real outcomes have not been reached. As a result, automated summarization is in high demand. Automatic summary takes several papers as input and outputs a condensed version, saving both information and time. The study was conducted in a single document and resulted in numerous publications. This report focuses on the frequency-based approach for text summarization.},
      keywords = {Automatic summarization, Extractive, frequency-based, Natural Language Processing.},
      month = {July},
  }