Skip to main content
SpringerLink
Log in

Personalized Abstract Review Summarization Using Personalized Key Information-Guided Network

  • Conference paper
  • First Online:
Inventive Computation and Information Technologies

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 336))

  • 978 Accesses

Abstract

We are proposing a personalized summarization model, which generates an abstractive summary of a random review based on the preference of a specific user. The summary will account the user’s preference on different aspects present in the review. We put forward a Personalized Key Information Guided Network (PKIGN) that pools both extractive and abstractive methods for summary generation. Specifically, keywords present in the review are extracted which are specific to that user, and these keywords are used as key information representation to guide the process of generating summaries. Additionally, Pointer-Guide mechanism is employed for obtaining long-term value for decoding. We evaluate our model on a new Trip-Advisor hotel review dataset, comprising of 140,874 reviews from 41,600 users. Combining the results from both human evaluation and quantitative analysis, it is seen that our model achieves better performance than existing models on personalized review summarization in case of hotel reviews.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. M.-T. Luong, H. Pham, C.D. Manning, Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025 (2015)

  2. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin, Attention is all you need, in Advances in Neural Information Processing Systems (NIPS), Dec 2017, pp. 5999–6009

    Google Scholar 

  3. S. Chopra, M. Auli, A.M. Rush, 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 (Association for Computational Linguistics, 2016), pp. 93–98

    Google Scholar 

  4. R. Móro, M. Bieliková, Personalized text summarization based on important terms identification, in 23rd International Workshop on Database and Expert Sytems Applications (IEEE, 2012), pp. 1529–4188

    Google Scholar 

  5. R. Mihalcea, P. Tarau, Textrank: bringing order into texts, in Proceedings of EMNLP 2004 (Association for Computational Linguistics, Barcelona, 2004), pp. 404–411

    Google Scholar 

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

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

  8. S. Takase, J. Suzuki, N. Okazaki, T. Hirao, M. Nagata, Neural headline generation on abstract meaning representation, in Empirical Methods in Natural Language Processing (2016)

    Google Scholar 

  9. R. Nallapati, B. Zhou, C. dos Santos, Ç. Gu̇lçehre, B. Xiang, Abstractive text summarization using sequence-to-sequence RNNs and beyond, in Computational Natural Language Learning (2016)

    Google Scholar 

  10. Y. Miao, P. Blunsom, Language as a latent variable: discrete generative models for sentence compression, in Empirical Methods in Natural Language Processing (2016)

    Google Scholar 

  11. L. Yu, J. Buys, P. Blunsom, Online segment to segment neural transduction, in Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (Association for Computational Linguistics, Austin, TX, 2016), pp. 1307–1316

    Google Scholar 

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

  13. Q. Zhou, N. Yang, F. Wei, M. Zhou, Selective encoding for abstractive sentence summarization, in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (2017), pp. 1095–1104

    Google Scholar 

  14. K. Yao, L. Zhang, D. Du, T. Luo, L. Tao, Y. Wu, Dual encoding for abstractive text summarization. IEEE Trans. Cybern. (2018), pp. 1–12

    Google Scholar 

  15. W. Lu, L. Wang, Neural network-based abstract generation for opinions and arguments, in NAACL (2016), pp. 47–57

    Google Scholar 

  16. D. Krishnan, P. Bharathy, Anagha, M. Venugopalan, A supervised approach for extractive text summarization using minimal robust features, in 2019 International Conference on Intelligent Computing and Control Systems (ICCS) (2019), pp. 521–527. https://doi.org/10.1109/ICCS45141.2019.9065651

  17. K. Shalini, H.B. Ganesh, M.A. Kumar, K. Soman, Sentiment analysis for codemixed Indian social media text with distributed representation, in 2018 International Conference on Advances in Computing, Communications and Informatics, ICACCI (IEEE, 2018), pp. 1126–1131

    Google Scholar 

  18. N. Lalithamani, R. Sukumaran, K. Alagamrnai, K.K. Sowmya, V. Divyalakshmi, S. Shanmugapriya, A mixed-initiative approach for summarizing discussions coupled with sentimental analysis, in Proceedings of the 2014 International Conference on Interdisciplinary Advances in Applied Computing (ACM, 2014), p. 5

    Google Scholar 

  19. M.V.K. Kiran, R.E. Vinodhini, R. Archanaa, K. Vimalkumar, User specific product recommendation and rating system by performing sentiment analysis on product reviews, in 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS) (2017), pp. 1–5. https://doi.org/10.1109/ICACCS.2017.8014640

  20. C. Li, W. Xu, S. Li, S. Gao, 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), vol. 2 (2018), pp. 55–60

    Google Scholar 

  21. J. Li, H. Li, C. Zong, Towards personalized review summarization via user-aware sequence network, in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33 (2019), pp. 6690–6697

    Google Scholar 

  22. J. Li, M. Ott, C. Cardie, Identifying manipulated offerings on review portals, in EMNLP (2013)

    Google Scholar 

  23. H. Wang, Y. Lu, C. Zhai, Latent aspect rating analysis on review text data: a rating regression approach, in SIGKDD (2010), pp. 783–792

    Google Scholar 

  24. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

  25. R. Pascanu, T. Mikolov, Y. Bengio, On the difficulty of training recurrent neural networks, in ICML (2013), pp. 1310–1318

    Google Scholar 

  26. C.-Y. Lin, Rouge: a package for automatic evaluation of summaries, in Text Summarization Branches Out (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India

    Nidhin S. Dharan & R. Gowtham

Authors
  1. Nidhin S. Dharan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Gowtham
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, RVS Technical Campus, Coimbatore, Tamil Nadu, India

    S. Smys

  2. Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  3. Business Administration Department, Gerald Schwartz School of Business, St. Francis Xavier University, Antigonish, NS, Canada

    Ram Palanisamy

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dharan, N.S., Gowtham, R. (2022). Personalized Abstract Review Summarization Using Personalized Key Information-Guided Network. In: Smys, S., Balas, V.E., Palanisamy, R. (eds) Inventive Computation and Information Technologies. Lecture Notes in Networks and Systems, vol 336. Springer, Singapore. https://doi.org/10.1007/978-981-16-6723-7_15

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-6723-7_15

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-6722-0

  • Online ISBN: 978-981-16-6723-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation