NLP TRANSFORMERS: ANALYSIS OF LLMS AND TRADITIONAL APPROACHES FOR ENHANCED TEXT SUMMARIZATION

Yunus Emre Işıkdemir

doi:10.31796/ogummf.1303569

TR EN

NLP TRANSFORMERS: ANALYSIS OF LLMS AND TRADITIONAL APPROACHES FOR ENHANCED TEXT SUMMARIZATION

Abstract

As the amount of the available information continues to grow, finding the relevant information has become increasingly challenging. As a solution, text summarization has emerged as a vital method for extracting essential information from lengthy documents. There are various techniques available for filtering documents and extracting the pertinent information. In this study, a comparative analysis is conducted to evaluate traditional approaches and state-of-the-art methods on the BBC News and CNN/DailyMail datasets. This study offers valuable insights for researchers to advance their research and helps practitioners in selecting the most suitable techniques for their specific use cases.

Keywords

Text Summarization , Transformers , NLP , LLM , Deep Learning

Kaynakça

Abdel-Salam, S., & Rafea, A. (2022). Performance study on extractive text summarization using BERT models. Information, 13(2), 67. https://doi.org/10.3390/info13020067.
Abdelaleem, N. M., Kader, H. A., & Salem, R. (2019). A brief survey on text summarization techniques. IJ of Electronics and Information Engineering, 10(2), 103-116.
Altmami, N. I., & Menai, M. E. B. (2022). Automatic summarization of scientific articles: A survey. Journal of King Saud University-Computer and Information Sciences, 34(4), 1011-1028.
Bansal, S., Kamper, H., Livescu, K., Lopez, A., & Goldwater, S. (2018). Low-resource speech-to-text translation. arXiv preprint arXiv:1803.09164. https://doi.org/10.48550/arXiv.1803.0916.
Bhandari, M., Gour, P., Ashfaq, A., Liu, P., & Neubig, G. (2020). Re-evaluating evaluation in text summarization. arXiv preprint arXiv:2010.07100. https://doi.org/10.48550/arXiv.2010.07100.
Cagliero, L., Garza, P., & Baralis, E. (2019). ELSA: A multilingual document summarization algorithm based on frequent itemsets and latent semantic analysis. ACM Transactions on Information Systems (TOIS), 37(2), 1-33. https://doi.org/10.1145/3298987.
Cai, T., Shen, M., Peng, H., Jiang, L., & Dai, Q. (2019). Improving transformer with sequential context representations for abstractive text summarization. In Natural Language Processing and Chinese Computing: 8th CCF International Conference, NLPCC 2019, Dunhuang, China, October 9–14, 2019, Proceedings, Part I (pp. 512-524). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-32233-5_40.
El-Kassas, W. S., Salama, C. R., Rafea, A. A., & Mohamed, H. K. (2021). Automatic text summarization: A comprehensive survey. Expert systems with applications, 165, 113679. https://doi.org/10.1016/j.eswa.2020.113679.
Goutte, C., & Gaussier, E. (2005). A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. In Advances in Information Retrieval: 27th European Conference on IR Research, ECIR 2005, Santiago de Compostela, Spain, March 21-23, 2005. Proceedings 27 (pp. 345-359). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-31865-1_25.
Greene, D., & Cunningham, P. (2006). Practical solutions to the problem of diagonal dominance in kernel document clustering. In Proceedings of the 23rd international conference on Machine learning (pp. 377-384). https://doi.org/10.1145/1143844.1143892.

Gupta, S., & Gupta, S. K. (2019). Abstractive summarization: An overview of the state of the art. Expert Systems with Applications, 121, 49-65. https://doi.org/10.1016/j.eswa.2018.12.011.
Hermann, K. M., Kocisky, T., Grefenstette, E., Espeholt, L., Kay, W., Suleyman, M., & Blunsom, P. (2015). Teaching machines to read and comprehend. Advances in neural information processing systems, 28.
Isikdemir, Y. E., & Yavuz, H. S. (2022). The scalable fuzzy inference-based ensemble method for sentiment analysis. Computational Intelligence and Neuroscience, 2022.
Joshi, A., Fidalgo, E., Alegre, E., & Fernández-Robles, L. (2019). SummCoder: An unsupervised framework for extractive text summarization based on deep auto-encoders. Expert Systems with Applications, 129, 200-215. https://doi.org/10.1016/j.eswa.2019.03.045.
Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., ... & Zettlemoyer, L. (2019). Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461. https://doi.org/10.48550/arXiv.1910.13461.
Li, C., Xu, W., Li, S., & Gao, S. (2018). Guiding generation for abstractive text summarization based on key information guide network. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) (pp. 55-60). https://doi.org/10.18653/v1/N18-2009.
Lin, C. Y. (2004). Rouge: A package for automatic evaluation of summaries. In Text summarization branches out (pp. 74-81).
Liu, S. H., Chen, K. Y., & Chen, B. (2020). Enhanced language modeling with proximity and sentence relatedness information for extractive broadcast news summarization. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), 19(3), 1-19. https://doi.org/10.1145/3377407.
Liu, Y., & Lapata, M. (2019). Text summarization with pretrained encoders. arXiv preprint arXiv:1908.08345. https://doi.org/10.48550/arXiv.1908.08345.
Liu, Y. (2019). Fine-tune BERT for extractive summarization. arXiv preprint arXiv:1903.10318. https://doi.org/10.48550/arXiv.1903.10318.
Manojkumar, V. K., Mathi, S., & Gao, X. Z. (2023). An Experimental Investigation on Unsupervised Text Summarization for Customer Reviews. Procedia Computer Science, 218, 1692-1701. https://doi.org/10.1016/j.procs.2023.01.147.
Nazari, N., & Mahdavi, M. A. (2019). A survey on automatic text summarization. Journal of AI and Data Mining, 7(1), 121-135. https://doi.org/10.22044/jadm.2018.6139.1726.
Papineni, K., Roukos, S., Ward, T., & Zhu, W. J. (2002). Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics (pp. 311-318).
Ramesh, A., Srinivasa, K. G., & Pramod, N. (2014). SentenceRank—a graph based approach to summarize text. In The Fifth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT 2014) (pp. 177-182). IEEE. https://doi.org/10.1109/ICADIWT.2014.6814680.
Ramina, M., Darnay, N., Ludbe, C., & Dhruv, A. (2020). Topic level summary generation using BERT induced Abstractive Summarization Model. In 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 747-752). IEEE. https://doi.org/10.1109/ICICCS48265.2020.9120997.
Rodríguez-Vidal, J., Carrillo-de-Albornoz, J., Amigó, E., Plaza, L., Gonzalo, J., & Verdejo, F. (2020). Automatic generation of entity-oriented summaries for reputation management. Journal of Ambient Intelligence and Humanized Computing, 11, 1577-1591. https://doi.org/10.1007/s12652-019-01255-9.
See, A., Liu, P. J., & Manning, C. D. (2017). Get to the point: Summarization with pointer-generator networks. arXiv preprint arXiv:1704.04368. https://doi.org/10.48550/arXiv.1704.04368.
Shi, T., Keneshloo, Y., Ramakrishnan, N., & Reddy, C. K. (2021). Neural abstractive text summarization with sequence-to-sequence models. ACM Transactions on Data Science, 2(1), 1-37. https://doi.org/10.1145/3419106.
Syed, A. A., Gaol, F. L., & Matsuo, T. (2021). A survey of the state-of-the-art models in neural abstractive text summarization. IEEE Access, 9, 13248-13265. https://doi.org/10.1109/ACCESS.2021.3052783.
Verma, P., & Om, H. (2019). A novel approach for text summarization using optimal combination of sentence scoring methods. Sādhanā, 44, 1-15. https://doi.org/10.1007/s12046-019-1082-4.
Verma, S., Gupta, N., Anil, B. C., & Chauhan, R. (2022). A Novel Framework for Ancient Text Translation Using Artificial Intelligence. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 11(4), 411-425. https://doi.org/10.14201/adcaij.28380.
Vhatkar, A., Bhattacharyya, P., & Arya, K. (2020). Knowledge graph and deep neural network for extractive text summarization by utilizing triples. In Proceedings of the 1st joint workshop on financial narrative processing and multiling financial summarisation (pp. 130-136).
Wang, Q., Liu, P., Zhu, Z., Yin, H., Zhang, Q., & Zhang, L. (2019). A text abstraction summary model based on BERT word embedding and reinforcement learning. Applied Sciences, 9(21), 4701. https://doi.org/10.3390/app9214701.
Widjanarko, A., Kusumaningrum, R., & Surarso, B. (2018). Multi document summarization for the Indonesian language based on latent dirichlet allocation and significance sentence. In 2018 International Conference on Information and Communications Technology (ICOIACT) (pp. 520-524). IEEE. https://doi.org/10.1109/ICOIACT.2018.8350668.
Widyassari, A. P., Rustad, S., Shidik, G. F., Noersasongko, E., Syukur, A., & Affandy, A. (2022). Review of automatic text summarization techniques & methods. Journal of King Saud University-Computer and Information Sciences, 34(4), 1029-1046. https://doi.org/10.1016/j.jksuci.2020.05.006.
Wu, Z., Lei, L., Li, G., Huang, H., Zheng, C., Chen, E., & Xu, G. (2017). A topic modeling based approach to novel document automatic summarization. Expert Systems with Applications, 84, 12-23. https://doi.org/10.1016/j.eswa.2017.04.054.
Yao, K., Zhang, L., Luo, T., & Wu, Y. (2018). Deep reinforcement learning for extractive document summarization. Neurocomputing, 284, 52-62. https://doi.org/10.1016/j.neucom.2018.01.020.
Zhang, X., Wei, F., & Zhou, M. (2019). HIBERT: Document level pre-training of hierarchical bidirectional transformers for document summarization. arXiv preprint arXiv:1905.06566. https://doi.org/10.48550/arXiv.1905.06566.
Zhang, J., Zhao, Y., Saleh, M., & Liu, P. (2020). Pegasus: Pre-training with extracted gap-sentences for abstractive summarization. In International Conference on Machine Learning (pp. 11328-11339). PMLR.
Zheng, J., Zhao, Z., Song, Z., Yang, M., Xiao, J., & Yan, X. (2020). Abstractive meeting summarization by hierarchical adaptive segmental network learning with multiple revising steps. Neurocomputing, 378, 179-188. https://doi.org/10.1016/j.neucom.2019.10.019.
Zhong, M., Liu, P., Chen, Y., Wang, D., Qiu, X., & Huang, X. (2020). Extractive summarization as text matching. arXiv preprint arXiv:2004.08795. https://doi.org/10.48550/arXiv.2004.08795.

Ayrıntılar

Birincil Dil

İngilizce

Konular

Bilgisayar Yazılımı

Bölüm

Araştırma Makalesi

Yazarlar

Yunus Emre Işıkdemir ^*
0000-0001-7022-2854
Türkiye

Erken Görünüm Tarihi

22 Nisan 2024

Yayımlanma Tarihi

22 Nisan 2024

Gönderilme Tarihi

27 Mayıs 2023

Kabul Tarihi

12 Mart 2024

Yayımlandığı Sayı

Yıl 2024 Cilt: 32 Sayı: 1

DOI

https://doi.org/10.31796/ogummf.1303569

IZ

https://izlik.org/JA53FK97UW

APA

Işıkdemir, Y. E. (2024). NLP TRANSFORMERS: ANALYSIS OF LLMS AND TRADITIONAL APPROACHES FOR ENHANCED TEXT SUMMARIZATION. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi, 32(1), 1140-1151. https://doi.org/10.31796/ogummf.1303569

AMA

1.Işıkdemir YE. NLP TRANSFORMERS: ANALYSIS OF LLMS AND TRADITIONAL APPROACHES FOR ENHANCED TEXT SUMMARIZATION. ESOGÜ Müh Mim Fak Derg. 2024;32(1):1140-1151. doi:10.31796/ogummf.1303569

Chicago

Işıkdemir, Yunus Emre. 2024. “NLP TRANSFORMERS: ANALYSIS OF LLMS AND TRADITIONAL APPROACHES FOR ENHANCED TEXT SUMMARIZATION”. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi 32 (1): 1140-51. https://doi.org/10.31796/ogummf.1303569.

EndNote

Işıkdemir YE (01 Nisan 2024) NLP TRANSFORMERS: ANALYSIS OF LLMS AND TRADITIONAL APPROACHES FOR ENHANCED TEXT SUMMARIZATION. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi 32 1 1140–1151.

IEEE

[1]Y. E. Işıkdemir, “NLP TRANSFORMERS: ANALYSIS OF LLMS AND TRADITIONAL APPROACHES FOR ENHANCED TEXT SUMMARIZATION”, ESOGÜ Müh Mim Fak Derg, c. 32, sy 1, ss. 1140–1151, Nis. 2024, doi: 10.31796/ogummf.1303569.

ISNAD

Işıkdemir, Yunus Emre. “NLP TRANSFORMERS: ANALYSIS OF LLMS AND TRADITIONAL APPROACHES FOR ENHANCED TEXT SUMMARIZATION”. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi 32/1 (01 Nisan 2024): 1140-1151. https://doi.org/10.31796/ogummf.1303569.

JAMA

1.Işıkdemir YE. NLP TRANSFORMERS: ANALYSIS OF LLMS AND TRADITIONAL APPROACHES FOR ENHANCED TEXT SUMMARIZATION. ESOGÜ Müh Mim Fak Derg. 2024;32:1140–1151.

MLA

Işıkdemir, Yunus Emre. “NLP TRANSFORMERS: ANALYSIS OF LLMS AND TRADITIONAL APPROACHES FOR ENHANCED TEXT SUMMARIZATION”. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi, c. 32, sy 1, Nisan 2024, ss. 1140-51, doi:10.31796/ogummf.1303569.

Vancouver

1.Yunus Emre Işıkdemir. NLP TRANSFORMERS: ANALYSIS OF LLMS AND TRADITIONAL APPROACHES FOR ENHANCED TEXT SUMMARIZATION. ESOGÜ Müh Mim Fak Derg. 01 Nisan 2024;32(1):1140-51. doi:10.31796/ogummf.1303569

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NLP TRANSFORMERS: ANALYSIS OF LLMS AND TRADITIONAL APPROACHES FOR ENHANCED TEXT SUMMARIZATION

TRANSFORMER MİMARİSİ TABANLI METİN ÖZETLEME VE DOĞAL DİL ANLAMLANDIRMA

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NLP TRANSFORMERS: ANALYSIS OF LLMS AND TRADITIONAL APPROACHES FOR ENHANCED TEXT SUMMARIZATION

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Leveraging LLMs for Smart Cities Qualitative Data Analysis