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Improving Text Summarization using Ensembled Approach based on Fuzzy with LSTM

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Abstract

Abstractive text summarization using attentional recurrent neural network (sequence-to-sequence) models have proven to be very effective. In this paper, a novel hybrid approach is presented for generating abstractive text summaries by combining fuzzy logic rules (which selects extractive sentences) with bidirectional long short-term memory (Bi-LSTM) which further produces abstractive summary. Bi-LSTM uses attention mechanism and Adam optimizer for updating network weights. The proposed approach utilizes fuzzy measures and inference to extract textual information from the document to find the most relevant sentences. These relevant sentences are given as input to Bi-LSTM to produce an abstractive summary of the significant sentences. The proposed FLSTM model is evaluated using ROUGE toolkit. The experiment is performed on standard datasets (i.e., DUC and CNN/daily mail). Another salient feature of this work is merging of DUC 2003–2004, DUC 2006–2007 datasets to generate a larger dataset to achieve better results. The FLSTM model is compared with other state-of-the-art models, and the empirical results suggested that the proposed FLSTM model outperforms all other models.

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References

  1. Hatzivassiloglou, V.; Klavans, J.L.; Eskin, E.: Detecting text similarity over short passages: Exploring linguistic feature combinations via machine learning. In: 1999 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora (1999)

  2. Lloret, E.; Palomar, M.: Text summarisation in progress: a literature review. Artif. Intell. Rev. 37(1), 1 (2012)

    Article  Google Scholar 

  3. Nenkova, A.; McKeown, K.: Mining Text Data, pp. 43–76. Springer, Berlin (2012)

  4. Azhari, M.; Jaya Kumar, Y.: Improving text summarization using neuro-fuzzy approach. J. Inf. Telecommun. 1(4), 367 (2017)

    Google Scholar 

  5. Lloret, E.: Paper supported by the Spanish Government under the project TEXT-MESS (TIN2006-15265-C06-01) (2008)

  6. Chopra, S.; Auli, M.; Rush, A.M.: Abstractive sentence summarization with attentive recurrent neural networks. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 93–98 (2016)

  7. Nallapati, R.; Zhou, B.; Gulcehre, C.; Xiang, B.; et al.: Abstractive text summarization using sequence-to-sequence RNNS and beyond (2016). arXiv preprint arXiv:1602.06023

  8. Young, T.; Hazarika, D.; Poria, S.; Cambria, E.: Recent trends in deep learning based natural language processing. IEEE Comput. Intell. Mag. 13(3), 55 (2018)

    Article  Google Scholar 

  9. Patil, P.D.; Kulkarni, N.: Text summarization using fuzzy logic. Int. J. Innovat. Res. Adv. Eng. 1, 3 (2014)

    Google Scholar 

  10. Suanmali, L.; Salim, N.; Binwahlan, M.S.: Fuzzy logic based method for improving text summarization (2009). arXiv preprint arXiv:0906.4690

  11. Goularte, F.B.; Nassar, S.M.; Fileto, R.; Saggion, H.: A text summarization method based on fuzzy rules and applicable to automated assessment. Expert Syst. Appl. 115, 264 (2019)

    Article  Google Scholar 

  12. Gambhir, M.; Gupta, V.: Recent automatic text summarization techniques: a survey. Artif. Intell. Rev. 47(1), 1 (2017)

    Article  Google Scholar 

  13. Cheng, J.; Lapata, M.: Neural summarization by extracting sentences and words (2016). arXiv preprint arXiv:1603.07252

  14. Zhang, Y.; Er, M.J.; Zhao, R.; Pratama, M.: Multiview convolutional neural networks for multidocument extractive summarization. IEEE Trans. Cybern. 47(10), 3230 (2016)

    Article  Google Scholar 

  15. Yao, K.; Zhang, L.; Du, D.; Luo, T.; Tao, L.; Wu, Y.: Dual encoding for abstractive text summarization. IEEE Trans. Cybern. 50, 985–996 (2018)

    Article  Google Scholar 

  16. Rush, A.M.; Chopra, S.; Weston, J.: A neural attention model for abstractive sentence summarization (2015). arXiv preprint arXiv:1509.00685

  17. Bahdanau, D.; Cho, K.; Bengio, Y.: Neural machine translation by jointly learning to align and translate (2014). arXiv preprint arXiv:1409.0473

  18. See, A.; Liu, P.J.; Manning, C.D.: Get to the point: Summarization with pointer-generator networks (2017). arXiv preprint arXiv:1704.04368

  19. Witte, R.; Bergler, S.: Fuzzy clustering for topic analysis and summarization of document collections. In: Conference of the Canadian Society for Computational Studies of Intelligence, pp. 476–488. Springer (2007)

  20. Kyoomarsi, F.; Khosravi, H.; Eslami, E.; Davoudi, M.: Extraction-based text summarization using fuzzy analysis. Iran. J. Fuzzy Syst. 7(3), 15 (2010)

    Google Scholar 

  21. Kiani-B, A.; Akbarzadeh-T, M.R.; Moeinzadeh, M.: Intelligent extractive text summarization using fuzzy inference systems. In: 2006 IEEE International Conference on Engineering of Intelligent Systems, pp. 1–4. IEEE (2006)

  22. Kyoomarsi, F.; Khosravi, H.; Eslami, E.; Dehkordy, P.K.; Tajoddin, A.: Optimizing text summarization based on fuzzy logic. In: Seventh IEEE/ACIS International Conference on Computer and Information Science (icis 2008), pp. 347–352. IEEE (2008)

  23. Suanmali, L.; Binwahlan, M.S.; Salim, N.: Sentence features fusion for text summarization using fuzzy logic. In: 2009 Ninth International Conference on Hybrid Intelligent Systems vol. 1, pp. 142–146. IEEE (2009)

  24. Leite, D.; Rino, L.: genetic fuzzy automatic text summarizer Csbc 2009. Inf. Ufrgs. Br 2007, 779 (2009)

    Google Scholar 

  25. Binwahlan, M.S.; Salim, N.; Suanmali, L.: Fuzzy swarm diversity hybrid model for text summarization. Inf. Process. Manag. 46(5), 571 (2010)

    Article  Google Scholar 

  26. Hannah, M.E.; Geetha, T.; Mukherjee, S.: Automatic extractive text summarization based on fuzzy logic: a sentence oriented approach. In: International Conference on Swarm, Evolutionary, and Memetic Computing, pp. 530–538. Springer (2011)

  27. Suanmali, L.; Salim, N.; Binwahlan, M.S.: Fuzzy genetic semantic based text summarization. In: 2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing, pp. 1184–1191. IEEE (2011)

  28. Dixit, R.S.; Apte, S.: Improvement of text summarization using fuzzy logic based method. IOSRJCE 5(6), 5 (2012)

    Article  Google Scholar 

  29. Megala, S.S.; Kavitha, A.; Marimuthu, A.: Enriching text summarization using fuzzy logic. Int. J. Comput. Sci. Inf. Technol. 5(1), 863 (2014)

    Google Scholar 

  30. Kumar, Y.J.; Salim, N.; Abuobieda, A.; Albaham, A.T.: Multi document summarization based on news components using fuzzy cross-document relations. Appl. Soft Comput. 21, 265 (2014)

    Article  Google Scholar 

  31. Megala, S.S.; Kavitha, A.; Marimuthu, A.: Text summarization system using fuzzy logic and conditional random field algorithm. Int. J. Comput. Sci. Inf. Technol. 1(5), 863–867 (2015)

    Google Scholar 

  32. Babar, S.; Patil, P.D.: Improving performance of text summarization. Proc. Comput. Sci. 46, 354 (2015)

    Article  Google Scholar 

  33. Abbasi-ghalehtaki, R.; Khotanlou, H.; Esmaeilpour, M.: Fuzzy evolutionary cellular learning automata model for text summarization. Swarm Evolut. Comput. 30, 11 (2016)

    Article  Google Scholar 

  34. Chopade, H.A.; Narvekar, M.: Hybrid auto text summarization using deep neural network and fuzzy logic system. In: 2017 International Conference on Inventive Computing and Informatics (ICICI), pp. 52–56. IEEE (2017)

  35. Lakshmi, S.S.; Rani, M.U.: Multi-Document Text Summarization Using Deep Learning Algorithm with Fuzzy Logic. In: 2018 IADS International Conference on Computing, Communications & Data Engineering (CCODE) (2018)

  36. Sahba, R.; Ebadi, N.; Jamshidi, M.; Rad, P.: Automatic text summarization using customizable fuzzy features and attention on the context and vocabulary. In: 2018 World Automation Congress (WAC), pp. 1–5. IEEE (2018)

  37. Uçkan, T.; Karcı, A.: Extractive multi-document text summarization based on graph independent sets. Egypt. Inf. J. 10.1016/j.eij.2019.12.002 (2020)

  38. Hark, C.; Karcı, A.: Karcı summarization: A simple and effective approach for automatic text summarization using Karcı entropy. Inf. Process. Manag. 57(3), 102187 (2020)

    Article  Google Scholar 

  39. Steinberger, J.; Ježek, K.: Evaluation measures for text summarization. Comput. Inf. 28(2), 251 (2012)

    Google Scholar 

  40. Lin, C.Y.: ROUGE: a package for automatic evaluation of summaries. In: Workshop on Text summarization branches out (WAS 2004) (2004)

  41. Nallapati, R.; Zhai, F.; Zhou, B.: Summarunner: a recurrent neural network based sequence model for extractive summarization of documents. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)

  42. Paulus, R.; Xiong, C.; Socher, R.: A deep reinforced model for abstractive summarization (2017). arXiv preprint arXiv:1705.04304

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Correspondence to Minakshi Tomer.

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Tomer, M., Kumar, M. Improving Text Summarization using Ensembled Approach based on Fuzzy with LSTM. Arab J Sci Eng 45, 10743–10754 (2020). https://doi.org/10.1007/s13369-020-04827-6

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