Skip to main content
SpringerLink
Log in

Multi-document hybrid text summarization with bi-LSTM RNN for Telugu language

  • Published:
Sādhanā Aims and scope Submit manuscript

Abstract

One of the most popular south Indian languages in India is the Telugu language which is currently spoken by 84 million native Telugu speakers in Andhra Pradesh and Telangana. With the rapid growth of the Telugu digital content, the need for the automatic text summarizer is arisen to provide short text from huge text documents. Extractive text summarization model generates only significant sentences. Abstractive text summarization method requires more training time. In this paper, a novel hybrid model is proposed for generating text summaries by combining extractive and abstractive approach to reduce the training time. For extractive method TextRank algorithm is utilized and for abstractive method attention-based sequence to sequence model with bidirectional long short-term memory (Bi-LSTM) is utilized. Moreover, coverage mechanism is included into the proposed hybrid approach to reduce the repetition in summaries and to improve the quality of summaries. The performance of the proposed hybrid model is evaluated by the ROUGE toolkit in terms of F-measure, recall and precision. The results of the proposed model are compared with other existing models which shows that the proposed hybrid model outperforms other existing text summarization models for Telugu Language.

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

Figure 1
Figure 2
Figure 3
Algorithm 1
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9

Similar content being viewed by others

Availability of data and materials

Data will be shared on the reasonable request.

References

  1. Alzuhair A and Al-Dhelaan M 2019 An approach for combining multiple weighting schemes and ranking methods in graph-based multi-document summarization. IEEE Access 7: 120375–120386

    Article  Google Scholar 

  2. Hernandez-Castaneda A, Garcia-Hernandez R A, Ledeneva Y and Millan-Hernandez C E 2020 Extractive automatic text summarization based on lexical-semantic keywords. IEEE Access 8: 49896–49907

    Article  Google Scholar 

  3. Elayeb B, Chouigui A, Bounhas M and Khiroun O B 2020 Automatic Arabic text summarization using analogical proportions. Cognit. Comput. 12(5): 1043–1069

    Article  Google Scholar 

  4. Tomer M and Kumar M 2020 Improving text summarization using ensembled approach based on fuzzy with LSTM. Arab. J. Sci. Eng. 45(12): 10743–10754

    Article  Google Scholar 

  5. Zhang P and Li C 2009 Automatic text summarization based on sentences clustering and extraction. In: 2nd IEEE International Conference on Computer Science and Information Technology, Beijing, China, pp. 167–170

  6. Wong K F, Wu M and Li W 2008 Extractive summarization using supervised and semi-supervised learning. In: 22nd International Conference on Computational Linguistics, Manchester, pp. 985–992

  7. Azadani M N, Ghadiri N and Davoodijam E 2018 Graph-based biomedical text summarization: an itemset mining and sentence clustering approach. J. Biomed. Inf. 84: 42–58

    Article  Google Scholar 

  8. Erkan G and Radev D R 2004 LexRank: graph-based lexical centrality as salience in text summarization. J. Artif. Intell. Res. 22: 457–479

    Article  Google Scholar 

  9. Mihalcea R and Tarau P 2004 TextRank: bringing order into texts. In: Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, Barcelona, Spain, pp. 404–411

  10. Pan H X, Liu H and Tang Y 2019 A sequence-to-sequence text summarization model with topic based attention mechanism. In:16th International Conference on Web Information Systems and Applications. Springer, Cham, pp. 285–297

  11. Zeng B, Xu R, Yang H, Gan Z and Zhou W 2020 Comprehensive document summarization with refined self-matching mechanism. Appl. Sci. 10(5): 1864

    Article  Google Scholar 

  12. Sun M, Liu Y, Liu Z and Zhang M 2018 Chinese computational linguistics and natural language processing based on naturally annotated big data. In: 17th China National Conference and 6th International Symposium, Changsha, China

  13. Tu Z, Lu Z, Liu Y, Liu X and Li H 2016 Modeling coverage for neural machine translation, pp. 76–85. arXiv:1601.04811v6

  14. Lin C Y 2004 ROUGE: a package for automatic evaluation of summaries. In: Proceedings of the ACL Workshop: Text Summarization Braches Out 2004, Barcelona, Spain

  15. Sutskever I, Vinyals O and Le Q V 2014 Sequence to sequence learning with neural networks. Adv. Neural Inf. Process. Syst. 27

  16. Bahdanau D, Cho K, Bengio Y 2014 Neural machine translation by jointly learning to align and translate. arXiv:1409.0473

  17. Rush A M, Chopra S, Weston J 2015 A neural attention model for abstractive sentence summarization. arXiv: 1509.00685

  18. See A, Liu P J and Manning C D 2017 Get to the point: summarization with pointer-generator networks. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pp. 1073–1083

  19. Khanam M H 2016 Text summarization for Telugu document. J. Comput. Eng. 25–28

  20. Shashikanth S and Sanghavi S 2019 Text summarization techniques survey on Telugu and Foreign languages. Int. J. Res. Eng. Sci. Manag. 2(1): 211–213

    Google Scholar 

  21. Kallimani J S, Srinivasa K G and Eswara Reddy B 2011 Information extraction by an abstractive text summarization for an Indian regional language. In: 7th International Conference on Natural Language Processing and Knowledge Engineering, Tokushima, Japan, pp. 319–322

  22. Sudha D N and Latha Y M 2020 Multi-document abstractive text summarization through semantic similarity matrix for Telugu language. Int. J. Adv. Sci. Technol. 29(1): 513–521

    Google Scholar 

  23. Vimal Kumar K and Yadav D 2015 An improvised extractive approach to Hindi text summarization. In: Information Systems Design and Intelligent Applications. Springer, New Delhi, pp. 291–300

  24. Gupta V and Kaur N 2016 A novel hybrid text summarization system for Punjabi text. Cogn. Comput. 8(2): 261–77

    Article  Google Scholar 

  25. Rathod Y V 2018 Extractive text summarization of Marathi news articles. Int. Res. J. Eng. Technol. 5(7): 1204–1210

    MathSciNet  Google Scholar 

  26. Banu M, Karthika C, Sudarmani P and Geetha T V 2007 Tamil document summarization using semantic graph method. In: International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007), Sivakasi, India, pp. 128–34

  27. Kondath M, Suseelan D P and Idicula S M 2022 Extractive summarization of Malayalam documents using latent Dirichlet allocation: an experience. J. Intell. Syst. 31(1): 393–406

    Google Scholar 

Download references

Acknowledgements

I confirm that all authors listed on the title page have contributed significantly to the work, have read the manuscript, attest to the validity and legitimacy of the data and its interpretation, and agree to its submission.

Funding

Funding information is not applicable because no funding was received

Author information

Authors and Affiliations

  1. Department of Information Technology, Anurag University, Hyderabad, Telangana, 500088, India

    G L Anand Babu

  2. Department of Computer Science and Engineering, Stanley College of Engineering and Technology for Women, Abids, Hyderabad, Telangana, 500001, India

    Srinivasu Badugu

Authors
  1. G L Anand Babu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Srinivasu Badugu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Experimental, literature reviews, identification of novelty, analyzing the data, code work, figures and tables works have been done by G. L. Anand Babu. Manuscript drafting, revision, proof reading and guidance of all works are done by Srinivasu Badugu.

Corresponding author

Correspondence to G L Anand Babu.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethics approval

Funding information is not applicable because no funding was received, so he name of the approving body and the approval number ID not applicable to this manuscript.

Consent to participate

This research did not contain any studies involving human animal participants.

Consent for publication

I, give my consent for the publication of above Article.I declare that I shall not submit the paper for publication in any other Journal or Magazine till the decision is made by journal editors.

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

Babu, G.L.A., Badugu, S. Multi-document hybrid text summarization with bi-LSTM RNN for Telugu language. Sādhanā 49, 155 (2024). https://doi.org/10.1007/s12046-024-02499-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12046-024-02499-8

Keywords

Search

Navigation