Text Summarization Techniques Using Natural Language Processing: A Systematic Literature Review

Rabia Tahseen, Uzma Omer, Muhammad Shoaib Farooq, Faiqa Adnan


In recent years, data has been growing rapidly in almost every domain. Due to this excessiveness of data, there is a need for an automatic text summarizer that summarizes long and numerical data especially textual data without losing its content. Text summarization has been under research for decades and researchers used different summarization methods by using natural language processing and combining various algorithms. This paper presents a systematic literature review by showing a survey of text summarization methods and explains the accuracy of these methods used for text summarization. The paper first introduced some concepts of extractive and abstractive text summarization and also define how deep learning models can be used for the improvement of text summarization. This paper aims to identify the current utilization of text summarization in different application domains. Different methodologies are discussed for text summarization. To carry out this SLR, twenty-four published articles have been chosen carefully for this domain. Moreover, it discusses issues and challenges which are investigated in different application domains using text summarization methods. Lastly, the existing work of different researchers has been carried out for further discussion.

Full Text:




“Dalwadi, Bijal Patel, Nikita, and Suthar Sanket, ‘A Review Paper on Text Summarization for Indian Languages’ IJSRD -International Journal for Scientific Research Development, Vol. 5, Issue 07, 2017.

Chandra Khatri, Sumanvoleti, Sathish Veeraraghavan, Nish Parikh, Atiq Islam, Shifa Mahmood, Neeraj Garg, and Vivek Singh, “Algorithmic Content Generation for Products”.

“Proceedings of IEEE International Conference on Big Data, Santa Clara, pp.2945-2947, CA 2015.”

Deepali K. Gaikwad and C. Namrata Mahender, "A Review Paper on Text Summarization”. International Journal of Advanced Research in Computer and Communication Engineering Vol. 5, Issue 3, March 2016.

Batra, S. Chaudhary, K. Bhatt, S. Varshney and S.Verma, "A Review: Abstractive Text Summarization Techniques using NLP," 2020 International Conference on Advances in Computing, Communication Materials (ICACCM), Dehradun, India, 2020, pp. 23-28, doi: 10.1109/ICACCM50413.2020.9213079.

Mehdi Allahyari, Seyedamin Pouriyeh, Mehdi Assefi, Saeid Safaei, Elizabeth D. Trippe, Juan B. Gutierrez, and Krys Kochut, “Text Summarization Techniques: A Brief Survey”. In Proceedings of arXiv, USA, July 2017.

Thomaidou, I. Lourentzou, P. Katsivelis-Perakis, and M. Vazirgiannis, "Automated Snippet Generation for Online Advertising", Proceedings of ACM International Conference on Information and Knowledge Management (CIKM’13), San Francisco, pp.1841- 1844, USA, 2013.

Huong Thanh Le and Tien Manh Le, "An approach to Abstractive Text Summarization", In proceeding of International Conference of Soft Computing and Pattern Recognition (SoCPaR), Hanoi, Vietnam, Dec 2013.

“Divya, Kasimahanthi, Kambala Sneha, Baisetti Sowmya, and G Sankara Rao.”

“TextSummarization Using Deep Learning’ 07, no. 05 (2020): 5.”

Sutskever, Ilya Vinyals, Oriol and Le, Quoc, “Sequence to Sequence Learning with Neural Networks”, Advances in Neural Information Processing Systems, 2014.

“Ramesh Nallapati, Bowen Zhou, Cicero Nogueira dos Santos, Caglar Gulcehre, and Bing Xiang, “Abstractive Text Summarizati.”

“The SIGNLL Conference on Computational Natural Language Learning (CoNLL), 2016.”

“Afzal and K. Mehmood, ‘Spam filtering of bi-lingual tweets using machine learning,’ in 2016 18th International Conferenc.”

A. Fattah, “A hybrid machine learning model for multi-document summarization,” Appl. Intell., vol. 40, no. 4, pp. 592–600, Jun. 2014, doi: 10.1007/s10489-013-0490-0.

Omer, U., Farooq, M. S., & Abid, A. (2021). Introductory programming course: review and future implications. PeerJ Computer Science, 7, e647.

“Ouhbi, Sofia, et al. ‘Requirements engineering education: a systematic mapping study.’ Requirements Engineering 20.2 (2015): 119-138.

Munot, Nikita, and Sharvari S. Govilkar. "Comparative study of text summarization methods." International Journal of Computer Applications 102.12 (2014).

Koupaee, Mahnaz, and William Yang Wang. "Wikihow: A large scale text summarization dataset." arXiv preprint arXiv1810.09305 (2018).

Suleiman, Dima, and Arafat Awajan. "Deep learning based abstractive text summarization: Approaches, datasets, evaluation measures, and challenges." Mathematical Problems in Engineering 2020 (2020).

“Jo, ‘K nearest neighbor for text summarization using feature similarity,’ in 2017 International Conference on Communicat.”

Ordonez, Y. Zhang, and S. L. Johnsson, “Scalable machine learning computing a data summarization matrix with a parallel array DBMS,” Distrib. Parallel Databases, vol. 37, no. 3, pp. 329–350, Sep. 2019, doi: 10.1007/s10619-018-7229-1.

Adhikari, Surabhi. "Nlp based machine learning approaches for text summarization." 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2020.

“Verma, Pradeepika, and Anshul Verma. ‘A review on text summarization techniques.’ Journal of Scientific Research 64.1 (2020).”

Tehseen, R., Farooq, M. S., & Abid, A. (2020). Earthquake prediction using expert systems: a systematic mapping study. Sustainability, 12(6), 2420.

Farooq, M. S., Riaz, S., Abid, A., Umer, T., & Zikria, Y. B. (2020). Role of IoT technology in agriculture: A systematic literature review. Electronics, 9(2), 319.

Farooq, M. S., Khan, S. A., Abid, K., Ahmad, F., Naeem, M. A., Shafiq3a, M., & Abid, A. (2015). Taxonomy and design considerations for comments in programming languages: a quality perspective. Journal of Quality and Technology Management, 10(2), 167-182.

Obaid, I., Farooq, M. S., & Abid, A. (2020). Gamification for recruitment and job training: model, taxonomy, and challenges. IEEE Access, 8, 65164-65178.

Farooq, M. S., Riaz, S., Abid, A., Abid, K., & Naeem, M. A. (2019). A Survey on the Role of IoT in Agriculture for the Implementation of Smart Farming. IEEE Access, 7, 156237-156271.

Abid, A., Hussain, N., Abid, K., Ahmad, F., Farooq, M. S., Farooq, U., ... & Sabir, N. (2016). A survey on search results diversification techniques. Neural Computing and Applications, 27(5), 1207-1229.

Naeem, A., Farooq, M. S., Khelifi, A., & Abid, A. (2020). Malignant melanoma classification using deep learning: datasets, performance measurements, challenges and opportunities. IEEE Access, 8, 110575-110597.

DOI: http://dx.doi.org/10.21015/vtse.v9i4.856


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.