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Opinion texts summarization based on texts concepts with multi-objective pruning approach

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Abstract

Considering the huge volume of opinion texts published on various social networks, it is extremely difficult to peruse and use these texts. The automatic creation of summaries can be a significant help for the users of such texts. The current paper employs manifold learning to mitigate the challenges of the complexity and high dimensionality of opinion texts and the K-Means algorithm for clustering. Furthermore, summarization based on the concepts of the texts can improve the performance of the summarization system. The proposed method is unsupervised extractive, and summarization is performed based on the concepts of the texts using the multi-objective pruning approach. The main parameters utilized to perform multi-objective pruning include relevancy, redundancy, and coverage. The simulation results show that the proposed method outperformed the MOOTweetSumm method while providing an improvement of 11% in terms of the ROGUE-1 measure and an improvement of 9% in terms of the ROGUE-L measure.

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Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

Notes

  1. Recall-Oriented Understudy for Gisting Evaluation.

References

  1. Pabbi K, Sindhu C (2021) Opinion summarisation using bi-directional long-short term memory. Sixth International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET).

  2. Murray G, Hoque E, Carenini G (2017) Sentiment analysis in social networks. Morgan Kaufmann

    Google Scholar 

  3. Aamir M, Ullah Jan A, Mukhtar N, Abid Khan M, Ali Z, Ahmed Abro W, Yurong G (2021) An unsupervised graph-based hybrid approach for opinion summarization. 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP).

  4. Varma V, Kurisinkel LJ, Radhakrishnan P (2017) A practical guide to sentiment analysis. Springer

    Google Scholar 

  5. Saeed RM, Rady S, Gharib TF (2021) Optimizing sentiment classification for Arabic opinion texts. Cogn Comput 13:164–178

    Article  Google Scholar 

  6. Saeed NMK, Helal NA, Badr NL, Gharib TF (2019) An enhanced feature-based sentiment analysis approach. Wiley Interdiscipl Rev: Data Min Knowl Discov 10:1–20

    Google Scholar 

  7. Golande A, Kamble R, Waghere S (2016) An overview of feature based opinion mining. Intell Syst Technol Appl 530:633–645

    Google Scholar 

  8. Moussa ME, Mohamed EH, Haggag MH (2018) A survey on opinion summarization techniques for social media. Fut Comput Inf J 3:82–109

    Google Scholar 

  9. Nathania G, Siautama R, Claire A, Suhartono D (2021) Extractive hotel review summarization based on TF/IDF and adjective-noun pairing by considering annual sentiment trends. Procedia Computer Sci 179:558–565

    Article  Google Scholar 

  10. Moratanch N, Chitrakala S (2017) A survey on extractive text summarization. IEEE International Conference on Computer, Communication, and Signal Processing (ICCCSP-2017).

  11. Moratanch N, Chitrakala S (2016) A survey on abstractive text summarization. International Conference on Circuit, Power and Computing Technologies [ICCPCT].

  12. Almatrafi Q, Johri A (2022) Improving MOOCs using information fromdiscussion forums: an opinion summarization and suggestion mining approach. IEEE Access 10:15565–15573

    Article  Google Scholar 

  13. Huang Y, Yu Zh, Xiang Y, Yu Zh, Guo J (2022) Exploiting comments information to improve legal public opinion news abstractive summarization. Front Comp Sci 16:1–10

    Google Scholar 

  14. Rani R, Lobiyal DK (2022) Document vector embedding based extractive text summarization system for Hindi and English text. Appl Intell 25:840

    Google Scholar 

  15. Modi Sh, Oza R (2018) Review on abstractive text summarization techniques (ATST) for single and multi documents. International Conference on Computing, Power and Communication Technologies (GUCON): 1173–1176.

  16. Naqeeb Khan S, Mohd Nawi N, Imrona M, Shahzad A, Ullah A, Rahman A (2018) Opinion mining summarization and automation process: a survey. Int J Adv Sci Eng Inf Technol 8:1836–1844

    Article  Google Scholar 

  17. Gupta S, Gupta SK (2019) Abstractive summarization: an overview of the state of the art. Expert Syst Appl 121:49–65

    Article  Google Scholar 

  18. Ouyang L, Li W, LiLu SQ (2018) Applying regression models to query-focused multi-document summarization. Inf Process Manag 47:227–237

    Article  Google Scholar 

  19. Mahak G, Vishal G (2017) Recent automatic text summarization techniques: a survey. Artif Intell Rev 48:1–66

    Google Scholar 

  20. Khan A, Salim N, Kuma YJ (2015) A framework for multi-document abstractive summarization based on semantic role labeling. Appl Soft Comput 30:737–747

    Article  Google Scholar 

  21. Rudra K, Ghosh S, Ganguly N, Goyal P, Ghosh S (2015) Extracting situational information from microblogs during disaster events: A classification-summarization approach. Proceedings of the 24th ACM International on Conference on Information and Knowledge Management: 583–592.

  22. Dutta S, Chandra V, Mehra K, Das AK, Chakraborty T, Ghosh S (2018) Ensemble algorithms for microblog summarization. IEEE Intell Syst 3:4–14

    Article  Google Scholar 

  23. Garg N, Favre B, Reidhammer K, Hakkani-Tur D (2009) ClusterRank: A graph based method for meeting summarization. Proceedings of the 10th International Conference of the International Speech Communication Association (Interspeech 2009): 1499–1502.

  24. Erkan G, Radev DR (2004) LexRank: graph-based lexical centrality as salience in text summarization. J Artif Intell 22:457–479

    Google Scholar 

  25. Gong Y, Liu X (2001) Generic text summarization using relevance measure and latent semantic analysis. Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval: 19–25.

  26. Luhn HP (1958) The automatic creation of literature abstracts. IBM J Res Dev 2:159–165

    Article  MathSciNet  Google Scholar 

  27. Radev DR, Hovy E, McKeown K (2002) Introduction to the special issue on summarization. Comput Linguist 28:399–440

    Article  Google Scholar 

  28. Nenkova A, Vanderwende L (2005) The impact of frequency on summarization. Microsoft Res. 101:87

    Google Scholar 

  29. He Z et al. (2012) Document summarization based on data reconstruction. Twenty-Sixth AAAI Conference on Artificial Intelligence: 620–626.

  30. Dutta S, Chandra V, Mehra K, Ghatak S, Das AK, Ghosh S (2019) Summarizing microblogs during emergency events: a comparison of extractive summarization algorithms. Emerg Technol Data Min Inf Secur 45:859–872

    Google Scholar 

  31. Alguliyev RM, Aluliyev RM, Isazadeh NR, Abdi A, Idris N (2016) COSUM: text summarization based on clustering and optimization. Expert Syst 6:1–17

    Google Scholar 

  32. Chakraborty R, Bhavsar M, Kumar Dandapat S, Chandra J (2019) Tweet summarization of news articles: an objective ordering-based perspective. IEEE Trans Comput Soc Syst 6:761–777

    Article  Google Scholar 

  33. Saini N, Saha S, Bhattacharyya P (2019) Multiobjective-based approach for microblog summarization. IEEE Trans Comput Soc Syst 6:1219–1231

    Article  Google Scholar 

  34. Dutta S, Das AK, Bhattacharya A, Dutta G, Parikh KK, Das A, Ganguly D (2019) Community detection based tweet summarization. emerging technologies in data mining and information security. Adv Intell Syst Comput 813:797–808

    Google Scholar 

  35. Zhou X, Wan X, Xiao J (2016) CMiner: opinion extraction and summarization for chinese microblogs. IEEE Trans Knowl Data Eng 28:1650–1663

    Article  Google Scholar 

  36. Jebrakumar R, Shrivastava S, Ramchandani H (2018) Multi post summarization using clustering technique. Proceedings of 4th International Conference on Energy Efficient Technologies for Sustainability.

  37. Niu J, Zhao Q, Wang L, Chen H, Zheng Sh (2016) Opinion summarization for short texts based on BM25 and syntactic parsing. 14th International Conference on Industrial Informatics (INDIN): 1177–1180.

  38. Waheeb SA, Khan NA, Chen B, Shang X (2020) Multidocument arabic text summarization based on clustering and word2vec to reduce redundancy. Information 11:1–13

    Google Scholar 

  39. Yu C, Shu L (2019) The Summarization of commodity short comments based on topic clustering. Journal of Physics: Conference Series 1302.

  40. Loret E, Boldrini E, Vodolazova T, Martínez-Barco P, Muñoz R, Palomar M (2015) A novel concept-level approach for ultra-concise opinion summarization. Expert Syst Appl 42:7148–7156

    Article  Google Scholar 

  41. Amplayo RK, Song M (2017) An adaptable fine-grained sentiment analysis for summarization of multiple short online reviews. Data Knowl Eng 110:54–67

    Article  Google Scholar 

  42. Bhatia S, Madan R, Yadav S L, Bhatia K (2019) An algoritmic approach based on principal component analysis for aspect-based opinion summarization. 6th International Conference on Computmohding for Sustainable Global Development (INDIACom): 874–879.

  43. Roul RK, Sahoo JK (2020) Sentiment analysis and extractive summarization based recommendation system. Comput Intell Data Min 990:473–487

    Google Scholar 

  44. Abdi C, Shasuddin SM, Hasan S (2018) Machine learning-based multi-documents sentiment-oriented summarization using linguistic treatment. Expert Syst Appl 109:66–85

    Article  Google Scholar 

  45. Ma Y, Li Q (2019) A weakly-supervised extractive framework for sentiment-preserving document summarization. World Wide Web 22:1401–1425

    Article  Google Scholar 

  46. Zhen Y, Fei Y, Kefeng F, Jian H (2017) Text dimensionality reduction with mutual information preserving mapping. Chin J Electron 26:919–925

    Article  Google Scholar 

  47. Pozzi FA, Fersini E, Messina E, Liu B (2017) Sentiment analysis in social networks. Morgan Kaufmann

    Book  Google Scholar 

  48. Lopez Gondori RE, Salaguerio Pardo TR (2017) Opinion summarization methods: comparing and extending extractive and abstractive approaches. Expert Syst Appl 7:124–134

    Article  Google Scholar 

  49. Rehioui H, Idrissi A (2020) New clustering algorithms for twitter sentiment analysis. IEEE Syst J 14:530–537

    Article  Google Scholar 

  50. Shih-Hung L, Kuan-Yu Ch, Berlin Ch, Hsin-Min W, Wen-Lian H (2017) Leveraging manifold learning for extractive broadcast news summarization. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): 5805–5809.

  51. Campos R, Mangaravite V, Pasquali A, Jorge A, Nunes C, Jatowt A (2020) YAKE! Keyword extraction from single documents using multiple local features. Inf Sci 509:257–289

    Article  Google Scholar 

  52. Wang R, Luo S, Pan L, Wu Zh, Yuan Y, Chen Q (2019) Microblog summarization using paragraph vector and semantic structure. Comput Speech Lang 57:1–19

    Article  Google Scholar 

  53. Jahanbakhsh Gudakahriz S, Eftekhari Moghadam AM, Mahmoudi F (2021) Opinion texts clustering using manifold learning based on sentiment and semantics analysis. Scientif Program 2021:5

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

    Sajjad Jahanbakhsh Gudakahriz & Amir Masoud Eftekhari Moghadam

  2. Advanced Analytics Department, General Motors Company, Warren, MI, USA

    Fariborz Mahmoudi

Authors
  1. Sajjad Jahanbakhsh Gudakahriz
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  2. Amir Masoud Eftekhari Moghadam
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  3. Fariborz Mahmoudi
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Contributions

All authors contributed to the ideation, theoretical development, and design of the research framework. SJ carried out the experiment under the supervision of AMEM and FM. All authors discussed the results and contributed to the final manuscript. Also, all authors confirm the results and the Manuscript content.

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Correspondence to Amir Masoud Eftekhari Moghadam.

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Gudakahriz, S.J., Moghadam, A.M.E. & Mahmoudi, F. Opinion texts summarization based on texts concepts with multi-objective pruning approach. J Supercomput 79, 5013–5036 (2023). https://doi.org/10.1007/s11227-022-04842-4

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