Skip to main content
SpringerLink
Log in

Economic news using LSTM and GRU models for text summarization in deep learning

  • Original Article
  • Published:
Journal of Data, Information and Management Aims and scope Submit manuscript

Abstract

This study innovatively addresses the field of text summarization in Natural Language Processing, focusing specifically on the Thai language. In a departure from the limited existing text summarization models for Thai, the researcher employs an approach, leveraging the maximum matching algorithm and Thai Character Cluster (TCC) from the PyThaiNLP library (version 2.2.4) to group Thai words according to the dictionary. The experimental setup utilizes a model incorporating Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models, with a unique exploration of varying maximum input text sizes—namely, 150, 500, and 700 words. Real-world data from Thai economic news articles, sourced from ThaiSum, consisting of 2,000 articles in the Thai language, forms the basis of the study. The evaluation framework employs the ROUGE scores (ROUGE-1, ROUGE-2, ROUGE-L) to assess efficiency. In the performance analysis for LSTM, the LSTM model, with a maximum input text of 500 words, emerges as the standout performer, securing the highest ROUGE-1, ROUGE-2, and ROUGE-L scores. Notably, the LSTM model achieves the highest ROUGE-1 recall (R) at 20.7. For the part of GRU, the GRU model, with a maximum input text of 700 words, also demonstrates robust performance, attaining the highest ROUGE-1, ROUGE-2, and ROUGE-L scores, with the highest ROUGE-1 recall (R) at 26.1. This research marks an advancement in text summarization for the Thai language, introducing innovative methods and models that contribute to the sparse landscape of existing approaches. The integration of the maximum matching algorithm, TCC, and the LSTM and GRU models, coupled with the meticulous evaluation using real Thai economic news data, positions this study as a valuable and pioneering contribution in the domain of Natural Language Processing.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

Data availability

The datasets generated during or analyzed during the current study are available in the ThaiSum (A dataset for Thai text summarization) repository, [https://github.com/nakhunchumpolsathien/ThaiSum].

References

  • Adelia R, Suyanto S, Wisesty UN (2019) Indonesian abstractive text summarization using bidirectional gated recurrent unit. Procedia Comput Sci 157:581–588

    Article  Google Scholar 

  • Brownlee J (2017) Deep learning for natural language processing: develop deep learning models for your natural language problems. Machine Learning Mastery. https://machinelearningmastery.com/deep-learning-for-nlp/

  • C. Blog, Understanding LSTM Networks (2015). http://colah.github.io/posts/2015-08-Understanding-LSTMs/

  • Chumpolsathien N (2020) Using knowledge distillation from keyword extraction to improve the informativeness of neural cross-lingual summarization. Master thesis, Beijing Institute of Technology

  • Deng L, Liu Y (eds) (2018) Deep learning in natural language processing. Springer, p 348

  • Divya K et al (2020) Text summarization using deep learning, vol 7. https://www.academia.edu/44229623/Text_Summarization_using_Deep_Learning

  • El-Kassas WS, Salama CR, Rafea AA, Mohamed HK (2021) Automatic text summarization: a comprehensive survey. Expert Syst Appl 165:113679

    Article  Google Scholar 

  • Fan X (2023) Artificial intelligence technology-based semantic sentiment analysis on network public opinion texts. Int J Inf Technol Syst Approach 16(2):1–14

    Article  Google Scholar 

  • Goyal P, Pandey S, Jain K (2018) Deep learning for natural language processing. Apress, New York

    Book  Google Scholar 

  • Greff K, Srivastava RK, Koutník J, Steunebrink BR, Schmidhuber J (2016) LSTM: a search space odyssey. IEEE Trans Neural Netw Learn Syst 28(10):2222–2232

    Article  MathSciNet  PubMed  Google Scholar 

  • Hernández-Castañeda Á, García-Hernández RA, Ledeneva Y (2023) Toward the automatic generation of an objective function for extractive text summarization. IEEE Access 11:51455–51464

    Article  Google Scholar 

  • Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  CAS  PubMed  Google Scholar 

  • Jozefowicz R, Zaremba W, Sutskever I (2015) An empirical exploration of recurrent network architectures. In: International conference on machine learning. In: Proceedings of the 32nd International Conference on Machine Learning (PMLR), pp 2342–2350

  • Jumpathong S, Theeramunkong T (2021) Automatic Thai news summarization using deep learning (Doctoral dissertation, Thammasat University)

  • Kerdvibulvech C (2023) A digital human emotion modeling application using metaverse technology in the post-COVID-19 era. HCI 19:480–489

    Google Scholar 

  • Khan B, Shah ZA, Usman M, Khan I, Niazi B (2023) Exploring the landscape of automatic text summarization: a comprehensive survey. IEEE Access 11:109819–109840

    Article  Google Scholar 

  • Kia MA (2023) Question-driven text summarization with extractive-abstractive frameworks. University of Essex, Colchester

    Google Scholar 

  • Lin CY (2004) ROUGE: a package for automatic evaluation of summaries. In: Text summarization branches out. Association for Computational Linguistics, pp 74–81

  • Noor M, Rana ZA (2023) Natural Language Processing (NLP) based extraction of tacit knowledge from written communication during software development. ICACS: 1–5

  • Phatthiyaphaibun W, Chaovavanich K, Polpanumas C, Suriyawongkol A, Lowphansirikul L, Chormai P (2016) PyThaiNLP: Thai natural language processing in python. Zenodo. https://doi.org/10.5281/zenodo.3519354

  • Rahman MM, Siddiqui FH (2019) An optimized abstractive text summarization model using peephole convolutional LSTM. Symmetry 11(10):1290

    Article  ADS  Google Scholar 

  • Rasrichai K, Chantarutai T, Kerdvibulvech C (2023) Recent roles of artificial intelligence artists in art circulation. Digital Society vol 2, no 15. Springer

  • Sirohi NK, Bansal M, Rajan SN (2021) Text summarization approaches using machine learning & LSTM. Rev Geintec-Gestao Inovacao Tecnol 11(4):5010–5026

    Google Scholar 

  • Socher R, Bengio Y, Manning CD (2012) Deep learning for NLP (without magic). In: Proceedings of the 50th annual meeting of the association for computational linguistics. Association for Computational Linguistics, pp 5–5

  • Song S, Huang H, Ruan T (2019) Abstractive text summarization using LSTM-CNN based deep learning. Multimed Tools Appl 78(1):857–875

    Article  Google Scholar 

  • Staudemeyer RC, Morris ER (2019) Understanding LSTM--a tutorial into long short-term memory recurrent neural networks. arXiv preprint arXiv:1909.09586

  • Sun D, Zhang X, Choo K-KR, Hu L, Wang F (2021) NLP-based digital forensic investigation platform for online communications. Comput Secur 104:102210

    Article  Google Scholar 

  • Tardy P (2021) Rouge. [Online]. Retrieved from https://github.com/pltrdy/rouge

  • Thaweephol K, Wiwatwattana N (2019) Long short-term memory deep neural network model for pm2. 5 forecasting in the bangkok urban area. In: 2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE). IEEE, pp 1–6

  • Vartiainen H, Tedre M (2023) Using artificial intelligence in craft education: crafting with text-to-image generative models. Digit Creativity 34(1):1–21

    Article  Google Scholar 

  • Yadav D, Desai J, Yadav AK (2022) Automatic text summarization methods: a comprehensive review. arXiv preprint arXiv:2204.01849

  • Yang S, Yu X, Zhou Y (2020) LSTM and GRU neural network performance comparison study: taking yelp review dataset as an example. In: 2020 International workshop on electronic communication and artificial intelligence (IWECAI). IEEE, pp 98–101

  • Zhang J, Zhu Y, Zhang X, Ye M, Yang J (2018) Developing a long short-term memory (LSTM) based model for predicting water table depth in agricultural areas. J Hydrol 561:918–929

    Article  Google Scholar 

  • Zhang M, Zhou G, Yu W, Huang N, Liu W (2022) A comprehensive survey of abstractive text summarization based on deep learning. Comput Intell Neurosci 2022

Download references

Funding

The authors did not receive support from any organization for the submitted work.

Author information

Authors and Affiliations

  1. Graduate School of Applied Statistics, National Institute of Development Administration, 148 Serithai Road, Klong-Chan, Bangkok, 10240, Thailand

    Kamonwan Tawong, Pichayapas Pholsukkarn, Pakanun Noawaroongroj & Thitirat Siriborvornratanakul

Authors
  1. Kamonwan Tawong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pichayapas Pholsukkarn
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pakanun Noawaroongroj
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Thitirat Siriborvornratanakul
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to this article equally.

Corresponding author

Correspondence to Thitirat Siriborvornratanakul.

Ethics declarations

Conflicts of interest

The researcher have no conflicts of interest to declare that are relevant to the content of this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tawong, K., Pholsukkarn, P., Noawaroongroj, P. et al. Economic news using LSTM and GRU models for text summarization in deep learning. J. of Data, Inf. and Manag. 6, 29–39 (2024). https://doi.org/10.1007/s42488-023-00111-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42488-023-00111-y

Keywords

Search

Navigation