Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{172276,
        author = {SOM GUPTA},
        title = {Machine Learning in Text Summarization},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {2696-2719},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=172276},
        abstract = {Automatic text summarization, an approach to generate summaries for informal text helps in saving time during information retrieval and other related tasks. Although various kinds of techniques like fuzzy logic, and other soft computing skills, NLP have been widely used to achieve this result, the paper analyses the research done in this field using supervised, unsupervised and reinforcement learning. The paper also analyses the works done using deep learning approaches for both the extractive and abstractive summarization. The paper compares the evaluation results obtained by various approaches for a particular dataset, discusses about the popular datasets used for the research purpose; pros and cons of the various approaches, open challenges and the future directions in this field of research, The paper cites the famous research works from sources like IEEE, Springer, ACL Anthology, ACM libraries to do the analysis. The paper serves as the beginning point for the novel researchers who wish to apply ML based approaches for the text summarization purpose.},
        keywords = {Machine Learning; Deep Learning; Text summarization; CNN; RNN; SVM; GRU},
        month = {January},
        }