Open Access
Issue
E3S Web Conf.
Volume 619, 2025
3rd International Conference on Sustainable Green Energy Technologies (ICSGET 2025)
Article Number 03005
Number of page(s) 9
Section Smart Electronics for Sustainable Solutions
DOI https://doi.org/10.1051/e3sconf/202561903005
Published online 12 March 2025
  1. Gambhir, M., & Gupta, V. (2017),“Text Summarization Techniques: A Brief Survey”, Journal of Big Data”, 4(1), 1-42.https://doi.org/10.1007/s41060-016-0024-4. [CrossRef] [Google Scholar]
  2. Lewis, M., Liu, Y., Goyal, N., et al. (2020),”BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension” Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), 7871–7880. https://arxiv.org/abs/1910.13461. [Google Scholar]
  3. Liu, Y., & Lapata, M. (2019).”Text Summarization with Pretrained Encoders”. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP-IJCNLP), https://arxiv.org/abs/1908.08345. [Google Scholar]
  4. See, A., Liu, P. J., & Manning, C. D. (2017),”Get to The Point: Summarization with Pointer-Generator Networks”, Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP), 107–117,https://arxiv.org/abs/1704.04368. [Google Scholar]
  5. Rush, A. M., Chopra, S., & Weston, J. (2015),”A Neural Attention Model for Abstractive Sentence Summarization”, Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), 379–389, https://arxiv.org/abs/1509.00685. [Google Scholar]
  6. Abualigah L., Bashabsheh M. Q., Alabool H., Shehab M., “Text summarization: a brief review”,Recent Advances in NLP: the case of Arabic languagep. 1-15, 2020. [Google Scholar]
  7. Afsharizadeh M., Ebrahimpour-Komleh H., Bagheri A., “Query-oriented text summarization using sentence extraction technique”, 2018 4th international conference on web research (ICWR), IEEE, p. 128-132, 2018. [Google Scholar]
  8. Alguliyev R. M., Aliguliyev R. M., Isazade N. R., Abdi A., Idris N., “COSUM: Text summarization based on clustering and optimization”, Expert Systems, vol. 36, no 1, p. e12340,2019. [CrossRef] [Google Scholar]
  9. Anand D., Wagh R., “Effective deep learning approaches for summarization of legal texts”,Journal of King Saud University-Computer and Information Sciences, vol. 34, no 5,p. 2141-2150, 2022. [CrossRef] [Google Scholar]
  10. Barbella M., Risi M., Tortora G., “A Comparison of Methods for the Evaluation of Text Summarization Techniques.”, DATA, p. 200-207, 2021. [Google Scholar]
  11. Barrios F., López F., Argerich L., Wachenchauzer R., “Variations of the similarity function of textrank for automated summarization”, arXiv preprint arXiv:1602.03606, 2016. [Google Scholar]
  12. Karthick, K., Aruna, S. K., & Ravivarman, S. Machine Learning Based Prediction of Social Media Performance Metrics Using Facebook Data. In Security and Risk Analysis for Intelligent Cloud Computing (pp. 216-233). CRC Press. https://doi.org/10.1201/9781003329947 [Google Scholar]
  13. Das A., Saha D., “Deep learning based Bengali question answering system using semantic textual similarity”, Multimedia Tools and Applications p. 1-25, 2022. [Google Scholar]
  14. El-Kassas W. S., Salama C. R., Rafea A. A., Mohamed H. K., “Automatic text summarization: A comprehensive survey”, Expert systems with applications, vol. 165, p. 113679, 2021. [CrossRef] [Google Scholar]
  15. Fabbri A. R., Krysci ´ nski W., McCann B., Xiong C., Socher R., Radev D., “Summeval: Re- evaluating summarization evaluation”, Transactions of the Association for Computational Linguistics, vol. 9, p. 391-409, 2021. [CrossRef] [Google Scholar]
  16. Ganesan K., Zhai C., Han J., “Opinosis: A graph-based approach to abstractive summarization of highly redundant opinions”, Proceedings of the 23rd international conference on computational linguistics (Coling 2010), p. 340-348, 2010. [Google Scholar]
  17. Goularte F. B., Nassar S. M., Fileto R., Saggion H., “A text summarization method based on fuzzy rules and applicable to automated assessment”, Expert Systems with Applications,vol. 115, p. 264-275, 2019. [CrossRef] [Google Scholar]
  18. Hariharan, Shanmugasundaram, D. Anandan, Murugaperumal Krishnamoorthy, Vinay Kukreja, Nitin Goyal, and Shih-Yu Chen. “Advancements in Liver Tumor Detection: A Comprehensive Review of Various Deep Learning Models.” CMES-Computer Modeling in Engineering & Sciences 142, no. 1 (2025). [Google Scholar]
  19. Hu R., Singh A., “Unit: Multimodal multitask learning with a unified transformer”, Proceedings of the IEEE/CVF International Conference on Computer Vision, p. 1439-1449, 2021. [Google Scholar]
  20. JUGRAN S., KUMAR A., TYAGI B. S., ANAND V., “Extractive automatic text summarization using SpaCy in Python & NLP”, 2021 International conference on advance computing and innovative technologies in engineering (ICACITE), IEEE, p. 582-585, 2021. [Google Scholar]
  21. Kumar Y., Kaur K., Kaur S., “Study of automatic text summarization approaches in different languages”, Artificial Intelligence Review, vol. 54, no 8, p. 5897-5929, 2021. [CrossRef] [Google Scholar]
  22. Lin C.-Y., “Recall oriented understudy of gisting evaluation”, 2005. [Google Scholar]
  23. Liu Y., Lapata M., “Text summarization with pretrained encoders”, arXiv preprint arXiv:1908.08345, 2019. [Google Scholar]
  24. Luhn H. P., “The automatic creation of literature abstracts”, IBM Journal of research and development, vol. 2, no 2, p. 159-165, 1958. [CrossRef] [Google Scholar]
  25. Rani, R.U., Swarupa, M.L. (2023). Contribution Title High Accuracy Dataset Control from Solar Photovoltaic Arrays by Decision Tree-Based System. In: Sharma, H., Shrivastava, V., Bharti, K.K., Wang, L. (eds) Communication and Intelligent Systems. ICCIS 2022. Lecture Notes in Networks and Systems, vol 689. Springer, Singapore. https://doi.org/10.1007/978-981-99-2322-9_39. [Google Scholar]

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