Current - Issue
Original Article
An AI Based Approach for Research Paper Summarization Using Deep Learning
Santhosh Kumar C1
Yogaraj S2
Yogeshwaran G3
Rishivanth D4
D. Praveen Kumar5
1 2 3 4 Department of Artificial Intelligence and Data Science, Gnanamani College of Technology (Autonomous), Namakkal, Tamil Nadu, India. 5 Assistant Professor, Department of Artificial Intelligence and Data Science, Gnanamani College of Technology (Autonomous), Namakkal, Tamil Nadu, India.
Published Online: May-August 2026
Pages: 885-893
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260502096
References
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2. P. Lopez, “GROBID: Combining automatic bibliographic data recognition and term extraction for scholarship publications,” in International Conference on Theory and Practice of Digital Libraries, 2009.
3. S. Klampfl and R. Kern, “An unsupervised machine learning approach to body text and table of contents extraction from digital scientific articles,” in International Conference on Theory and Practice of Digital Libraries, 2013, pp. 144–155.
4. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in NAACL, 2019.
5. Y. Xu, M. Li, L. Cui, S. Huang, F. Wei, and M. Zhou, “LayoutLM: Pre-training of text and layout for document image understanding,” in KDD, 2020, pp. 1192–1200.
6. Z. Xu, R. Jain, and M. Shah, “DocFormer: End-to-end transformer for document understanding,” in ICCV, 2021.
7. T. Brown et al., “Language models are few-shot learners,” in NeurIPS, 2020.
8. I. Beltagy, K. Lo, and A. Cohan, “SciBERT: A pretrained language model for scientific text,” in EMNLP, 2019.
9. J. Maynez, S. Narayan, B. Bohnet, and R. McDonald, “On faithfulness and factuality in abstractive summarization,” in ACL, 2020, pp. 1906–1919.
10. P. Lewis et al., “Retrieval-augmented generation for knowledge-intensive NLP tasks,” in NeurIPS, 2020.
11. G. Mariani, F. Scheidegger, R. Istrate, C. Bekas, and C. Malossi, “Data validation in machine learning pipelines,” IEEE Transactions on Knowledge and Data Engineering, 2019.
12. G. Tsatsaronis et al., “An overview of the BioASQ large-scale biomedical semantic indexing and question answering competition,” BMC Bioinformatics, vol. 16, no. 138, 2015.
13. Q. Jin, B. Dhingra, Z. Liu, W. Cohen, and X. Lu, “PubMedQA: A dataset for biomedical research question answering,” in EMNLP-IJCNLP, 2019, pp. 2567–2577.
14. DeepSeek-AI, “DeepSeek-V3 technical report,” arXiv preprint arXiv: 2412.19437, 2024.
15. T. Cao, N. Raman, D. Dervovic, and C. Tan, “Characterizing multimodal long-form summarization: A case study on financial reports,” arXiv preprint arXiv: 2404.06162, 2024.
16. S. Manic K., A. Al-Balushi, A. Al-Bemani, S. Al Araimi, B. G., U. Suresh, and A. Najeeb, “AI-driven multi-modal information synthesis: Integrating PDF querying, speech summarization, and cross-language text summarization,” Procedia Computer Science, 2024.
17. S. Kumar, G. S. Kohli, T. Ghosal, and A. Ekbal, “Longform multimodal lay summarization of scientific papers: Towards automatically generating science blogs from research articles,” in Proc. LREC-COLING, 2024, pp. 10790–10801.
2. P. Lopez, “GROBID: Combining automatic bibliographic data recognition and term extraction for scholarship publications,” in International Conference on Theory and Practice of Digital Libraries, 2009.
3. S. Klampfl and R. Kern, “An unsupervised machine learning approach to body text and table of contents extraction from digital scientific articles,” in International Conference on Theory and Practice of Digital Libraries, 2013, pp. 144–155.
4. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in NAACL, 2019.
5. Y. Xu, M. Li, L. Cui, S. Huang, F. Wei, and M. Zhou, “LayoutLM: Pre-training of text and layout for document image understanding,” in KDD, 2020, pp. 1192–1200.
6. Z. Xu, R. Jain, and M. Shah, “DocFormer: End-to-end transformer for document understanding,” in ICCV, 2021.
7. T. Brown et al., “Language models are few-shot learners,” in NeurIPS, 2020.
8. I. Beltagy, K. Lo, and A. Cohan, “SciBERT: A pretrained language model for scientific text,” in EMNLP, 2019.
9. J. Maynez, S. Narayan, B. Bohnet, and R. McDonald, “On faithfulness and factuality in abstractive summarization,” in ACL, 2020, pp. 1906–1919.
10. P. Lewis et al., “Retrieval-augmented generation for knowledge-intensive NLP tasks,” in NeurIPS, 2020.
11. G. Mariani, F. Scheidegger, R. Istrate, C. Bekas, and C. Malossi, “Data validation in machine learning pipelines,” IEEE Transactions on Knowledge and Data Engineering, 2019.
12. G. Tsatsaronis et al., “An overview of the BioASQ large-scale biomedical semantic indexing and question answering competition,” BMC Bioinformatics, vol. 16, no. 138, 2015.
13. Q. Jin, B. Dhingra, Z. Liu, W. Cohen, and X. Lu, “PubMedQA: A dataset for biomedical research question answering,” in EMNLP-IJCNLP, 2019, pp. 2567–2577.
14. DeepSeek-AI, “DeepSeek-V3 technical report,” arXiv preprint arXiv: 2412.19437, 2024.
15. T. Cao, N. Raman, D. Dervovic, and C. Tan, “Characterizing multimodal long-form summarization: A case study on financial reports,” arXiv preprint arXiv: 2404.06162, 2024.
16. S. Manic K., A. Al-Balushi, A. Al-Bemani, S. Al Araimi, B. G., U. Suresh, and A. Najeeb, “AI-driven multi-modal information synthesis: Integrating PDF querying, speech summarization, and cross-language text summarization,” Procedia Computer Science, 2024.
17. S. Kumar, G. S. Kohli, T. Ghosal, and A. Ekbal, “Longform multimodal lay summarization of scientific papers: Towards automatically generating science blogs from research articles,” in Proc. LREC-COLING, 2024, pp. 10790–10801.
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