Soft Computing

, Volume 23, Issue 24, pp 13183–13204 | Cite as

A nifty review to text summarization-based recommendation system for electronic products

  • Rajendra Kumar RoulEmail author
  • Kushagr Arora
Methodologies and Application


With the commencement of new technology, demands of online shopping are increasing day by day and hence an electronic product receives a huge number of customers reviews everyday. Because of this, a customer who wants to buy a particular product face difficulty as he needs to go through all the reviews of that product before taking a final decision. Automatically generated summary of the reviews could aid the customers in selecting the appropriate product. Aiming in this direction, a novel approach for making automatic extractive text summaries of the reviews for various electronic products is proposed in this paper. We have taken into account both the content of the review and the author’s credibility while evaluating the importance of a sentence. Both the content and semantic similarities are measured between every pair of sentences of a review. In order to form the summary of the reviews, fuzzy c-means clustering is used. For experimental purpose, Amazon dataset is used and the results indicate that the proposed method outperforms some of the baseline methods for generating the summary of the reviews, thus providing more concrete and robust summary.


Content similarity Fuzzy clustering Recommendation Review Semantic similarity Text summarization 


Compliance with ethical standards

Conflict of interest

All authors have declared that they have no conflicts of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. Abdi A, Idris N, Alguliyev RM, Aliguliyev RM (2017) Query-based multi-documents summarization using linguistic knowledge and content word expansion. Soft Comput 21(7):1785–1801CrossRefGoogle Scholar
  2. Abdi A, Shamsuddin SM, Hasan S, Piran J (2018) Automatic sentiment-oriented summarization of multi-documents using soft computing. Soft Comput 1–18Google Scholar
  3. Adomavicius G, Tuzhilin A (2011) Context-aware recommender systems. In: Recommender systems handbook, Springer, pp 217–253Google Scholar
  4. Aggarwal CC (2016) Ensemble-based and hybrid recommender systems. In: Recommender systems, Springer, pp 199–224Google Scholar
  5. Al\(_{-}\)Janabi S (2018) Smart system to create an optimal higher education environment using ida and iots. In: International journal of computers and applications, Taylor & Francis, pp 1–16Google Scholar
  6. Al\(_{-}\)Janabi S, Al\(_{-}\)Shourbaji I, Salman MA (2018) Assessing the suitability of soft computing approaches for forest fires prediction. Appl Comput Inf 14(2): 214–224Google Scholar
  7. Al\(_{-}\)Janabi S, Fatma R (2019) Intelligent big data analysis to design smart predictor for customer churn in telecommunication industry. In: Farhaoui Y, Moussaid L (eds) ICBDSDE 2018, SBD 53. Springer, Switzerland, pp 246–272Google Scholar
  8. Al\(_{-}\)Janabi S, Salman MA, Fanfakh A (2018) Recommendation system to improve time management for people in education environments. J Eng Appl Sci 13(24): 10 182–10 193Google Scholar
  9. Al\(_{-}\)Janabi S, Salman MA, Mohmmad M (2019) Multi-level network construction based on intelligent big data analysis. In: Farhaoui Y, Moussaid L (eds) ICBDSDE 2018, SBD 53, Springer, Switzerland , pp 102–118Google Scholar
  10. Ali SH (2012) A novel tool (fp-kc) for handle the three main dimensions reduction and association rule mining. In: 6th international conference on sciences of electronics, technologies of information and telecommunications (SETIT), 2012. IEEE, pp 951–961Google Scholar
  11. Bezdek JC, Ehrlich R, Full W (1984) Fcm: The fuzzy c-means clustering algorithm. Comput Geosci 10(2):191–203CrossRefGoogle Scholar
  12. Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowledge-based Syst 46:109–132CrossRefGoogle Scholar
  13. Breese JS, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth conference on uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc., pp 43–52Google Scholar
  14. Cao Y, Li Y (2007) An intelligent fuzzy-based recommendation system for consumer electronic products. Expert Syst Appl 33(1):230–240CrossRefGoogle Scholar
  15. Chali Y, Hasan SA, Joty SR (2009) A svm-based ensemble approach to multi-document summarization. In: Advances in artificial intelligence, Springer, pp 199–202Google Scholar
  16. Chen Y-L, Cheng L-C, Chuang C-N (2008) A group recommendation system with consideration of interactions among group members. Expert Syst Appl 34(3):2082–2090CrossRefGoogle Scholar
  17. Cilibrasi RL, Vitanyi PM (2007) The Google similarity distance. IEEE Trans Knowl Data Eng 19(3):1–15CrossRefGoogle Scholar
  18. Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26(3):297–302CrossRefGoogle Scholar
  19. Dietterich TG, Wettschereck D, Atkeson CG, Moore AW (1994) Memory-based methods for regression and classification. In: Advances in neural information processing systems, pp 1165–1166Google Scholar
  20. Fattah MA (2014) A hybrid machine learning model for multi-document summarization. Appl Intell 40(4):592–600CrossRefGoogle Scholar
  21. Galanis D, Lampouras G, Androutsopoulos I (2012) Extractive multi-document summarization with integer linear programming and support vector regression. In: COLING, Citeseer, pp 911–926Google Scholar
  22. Ganesan K, Zhai C, Han J (2010) Opinosis: a graph-based approach to abstractive summarization of highly redundant opinions. In: Proceedings of the 23rd international conference on computational linguistics. Association for Computational Linguistics, pp 340–348Google Scholar
  23. Gavalas D, Kenteris M (2011) A web-based pervasive recommendation system for mobile tourist guides. Pers Ubiquitous Comput 15(7):759–770CrossRefGoogle Scholar
  24. Gong Y, Liu X (2001) Generic text summarization using relevance measure and latent semantic analysis. In: Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, ACM, pp 19–25Google Scholar
  25. Gupta V, Lehal GS (2010) A survey of text summarization extractive techniques. J Emerg Technol Web Intell 2(3):258–268Google Scholar
  26. Hennig-Thurau T, Wiertz C, Feldhaus F (2015) Does twitter matter? The impact of microblogging word of mouth on consumers adoption of new movies. J Acad Market Sci 43(3):375–394CrossRefGoogle Scholar
  27. He Q, Pei J, Kifer D, Mitra P, Giles L (2010) Context-aware citation recommendation. In: Proceedings of the 19th international conference on World wide web, ACM, pp 421–430Google Scholar
  28. Huang X, Wan X, Xiao J (2014) Comparative news summarization using concept-based optimization. Knowled Inf Syst 38(3):691–716CrossRefGoogle Scholar
  29. Jones K Sparck (1972) A statistical interpretation of term specificity and its application in retrieval. J Doc 28(1):11–21CrossRefGoogle Scholar
  30. Kabadjov M, Steinberger J, Steinberger R (2013) Multilingual statistical news summarization. In: Multi-source, multilingual information extraction and summarization. Springer, pp 229–252Google Scholar
  31. Kalajdzic K, Ali SH, Patel A (2015) Rapid lossless compression of short text messages. Comput Stand Interfaces 37:53–59CrossRefGoogle Scholar
  32. Kim E, Sung Y, Kang H (2014) Brand followers retweeting behavior on twitter: How brand relationships influence brand electronic word-of-mouth. Comput Hum Behav 37:18–25CrossRefGoogle Scholar
  33. Liu C-L, Hsaio W-H, Lee C-H, Lu G-C, Jou E (2012) Movie rating and review summarization in mobile environment. IEEE Trans Syst Man Cybern Part C (Appl Rev) 42(3):397–407CrossRefGoogle Scholar
  34. Lynn HM, Choi C, Kim P (2018) An improved method of automatic text summarization for web contents using lexical chain with semantic-related terms. Soft Comput 22(12):4013–4023CrossRefGoogle Scholar
  35. Mei J-P, Chen L (2012) Sumcr: a new subtopic-based extractive approach for text summarization. Knowl Inf Syst 31(3):527–545CrossRefGoogle Scholar
  36. Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. CoRR, vol. abs/1301.3781Google Scholar
  37. Miller GA (1995) Wordnet: a lexical database for english. Commun ACM 38(11):39–41CrossRefGoogle Scholar
  38. Moratanch N, Chitrakala S (2017) A survey on extractive text summarization. In: IEEE International conference on computer, communication and signal processing (ICCCSP), 2017, pp 1–6Google Scholar
  39. Nallapati R, Zhou B, dos Santos C, glar Gulçehre Ç, Xiang B (2016) Abstractive text summarization using sequence-to-sequence rnns and beyond. In: CoNLL 2016, p 280Google Scholar
  40. Pazzani MJ, Billsus D (2007) Content-based recommendation systems. In: The adaptive web, Springer, pp 325–341Google Scholar
  41. Pazzani MJ (1999) A framework for collaborative, content-based and demographic filtering. Artif Intell Rev 13(5–6):393–408CrossRefGoogle Scholar
  42. Peetz M-H, de Rijke M, Kaptein R (2016) Estimating reputation polarity on microblog posts. Inf Process Manage 52(2):193–216CrossRefGoogle Scholar
  43. Quijano-Sánchez L, Díaz-Agudo B, Recio-García JA (2014) Development of a group recommender application in a social network. Knowledge-Based Syst 71:72–85CrossRefGoogle Scholar
  44. Roul RK, Mehrotra S, Pungaliya Y, Sahoo JK (2019) A new automatic multi-document text summarization using topic modeling. In: International conference on distributed computing and internet technology, vol 11319. LNCS, Springer, pp 212–221Google Scholar
  45. Roul RK, Sahoo JK, Goel R (2017) Deep learning in the domain of multi-document text summarization. In: International conference on pattern recognition and machine intelligence, vol 10597. LNCS, Springer, pp 575–581Google Scholar
  46. Roul RK, Asthana SR, Kumar G (2017) Study on suitability and importance of multilayer extreme learning machine for classification of text data. Soft Comput 21(15):4239–4256CrossRefGoogle Scholar
  47. Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65CrossRefGoogle Scholar
  48. Shehata S, Karray F, Kamel M (2010) An efficient concept-based mining model for enhancing text clustering. IEEE Trans Knowl Data Eng 22(10):1360–1371CrossRefGoogle Scholar
  49. Tang J, Hu X, Liu H (2013) Social recommendation: a review. Soc Netw Anal Min 3(4):1113–1133CrossRefGoogle Scholar
  50. Trewin S (2000) Knowledge-based recommender systems. Encycl Libr Inf Sci 69(Supplement 32):180Google Scholar
  51. Valizadeh M, Brazdil P (2014) Exploring actor–object relationships for query-focused multi-document summarization. Soft Comput 1–13Google Scholar
  52. West JD, Wesley-Smith I, Bergstrom CT (2016) A recommendation system based on hierarchical clustering of an article-level citation network. IEEE Trans Big Data 2(2):113–123CrossRefGoogle Scholar
  53. Yang G, Wen D, Chen N-S, Sutinen E et al (2015) A novel contextual topic model for multi-document summarization. Expert Syst Appl 42(3):1340–1352CrossRefGoogle Scholar
  54. Yousefi-Azar M, Hamey L (2017) Text summarization using unsupervised deep learning. Expert Syst Appl 68:93–105CrossRefGoogle Scholar
  55. Zhang Y, Koren J (2007) Efficient bayesian hierarchical user modeling for recommendation system. In: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, ACM, pp 47–54Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceThapar Institute of Engineering and TechnologyPatialaIndia
  2. 2.Department of Computer ScienceBITS Pilani-K.K.Birla Goa CampusZuarinagarIndia

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