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Microblog summarization using self-adaptive multi-objective binary differential evolution

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Abstract

Social media platforms become paramount for gathering relevant information during the occurrence of any natural disaster. Twitter has emerged as a platform which is heavily used for the purpose of communication during disaster events. Therefore, it becomes necessary to design a technique which can summarize the relevant tweets and thus, can help in the decision-making process of disaster management authority. In this paper, the problem of summarizing the relevant tweets is posed as an optimization problem where a subset of tweets is selected using the search capability of multi-objective binary differential evolution (MOBDE) by optimizing different perspectives of the summary. MOBDE deals with a set of solutions in its population, and each solution encodes a subset of tweets. Three versions of the proposed approach, namely, MOOTS1, MOOTS2, and MOOTS3, are developed in this paper. They differ in the way of working and the adaptive selection of parameters. Recently developed self-organizing map based genetic operator is explored in the optimization process. Two measures capturing the similarity/dissimilarity between tweets, word mover distance and BM25 are explored in the optimization process. The proposed approaches are evaluated on four datasets related to disaster events containing only relevant tweets. It has been observed that all versions of the developed MOBDE framework outperform the state-of-the-art (SOA) techniques. In terms of improvements, our best-proposed approach (MOOST3) improves by 8.5% and 3.1% in terms of ROUGE− 2 and ROUGE−L, respectively, over the existing techniques and these improvements are further validated using statistical significance t-test.

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  1. http://crisisnlp.qcri.org/lrec2016/lrec2016.html

References

  1. Alguliev RM, Aliguliyev RM, Isazade NR (2012) Desamc+ docsum: Differential evolution with self-adaptive mutation and crossover parameters for multi-document summarization. Knowl-Based Syst 36:21–38

  2. Ali SM, Noorian Z, Bagheri E, Ding C, Al-Obeidat F (2020) Topic and sentiment aware microblog summarization for twitter. J Intell Inf Syst 54(1):129–156

  3. Amato F, Castiglione A, Moscato V, Picariello A, Sperlì G (2018) Multimedia summarization using social media content. Multimed Tools Appl 77(14):17,803–17,827

  4. Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3(Jan):993–1022

  5. Cameron MA, Power R, Robinson B, Yin J (2012) Emergency situation awareness from twitter for crisis management. In: Proceedings of the 21st International Conference on World Wide Web. ACM, pp 695–698

  6. Das S, Suganthan PN (2011) Differential evolution: A survey of the state-of-the-art. IEEE Trans Evol Comput 15(1):4–31

  7. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans Evol Comput 6(2):182–197

  8. Dong R, Li L, Zhang Q, Cai G (2018) Information diffusion on social media during natural disasters. IEEE Transactions on Comput Soc Syst 5(1):265–276

  9. Dutta S, Chandra V, Mehra K, Das AK, Chakraborty T, Ghosh S (2018) Ensemble algorithms for microblog summarization. IEEE Intelligent Systems 33(3):4–14

  10. Dutta S, Chandra V, Mehra K, Ghatak S, Das AK, Ghosh S (2019) Summarizing microblogs during emergency events: A comparison of extractive summarization algorithms. In: Emerging Technologies in Data Mining and Information Security. Springer, pp 859–872

  11. Erkan G, Radev DR (2004) Lexrank: Graph-based lexical centrality as salience in text summarization. J Artif Intell Res 22:457–479

  12. Garg N, Favre B, Reidhammer K, Hakkani-Tür D (2009) Clusterrank: a graph based method for meeting summarization. In: Tenth Annual Conference of the International Speech Communication Association

  13. Geng X, Zhang Y, Jiao Y, Mei Y (2019) A novel hybrid clustering algorithm for topic detection on chinese microblogging. IEEE Transactions on Computational Social Systems pp 1–12, https://doi.org/10.1109/TCSS.2019.2897641

  14. He Z, Chen C, Bu J, Wang C, Zhang L, Cai D, He X (2012) Document summarization based on data reconstruction. In: AAAI

  15. Huang TCK, Chen YL, Chang TH (2015) A novel summarization technique for the support of resolving multi-criteria decision making problems. Decis Support Syst 79:109–124

  16. Imran M, Castillo C, Lucas J, Meier P, Vieweg S (2014) Aidr: Artificial intelligence for disaster response. In: Proceedings of the 23rd International Conference on World Wide Web. ACM, pp 159–162

  17. Imran M, Castillo C, Diaz F, Vieweg S (2015) Processing social media messages in mass emergency: A survey. ACM Comput Surv (CSUR) 47(4):67

  18. Jiang S, Yang S, Wang Y, Liu X (2018) Scalarizing functions in decomposition-based multiobjective evolutionary algorithms. IEEE Trans Evol Comput 22(2):296–313

  19. Kang Q, Song X, Zhou M, Li L (2018) A collaborative resource allocation strategy for decomposition-based multiobjective evolutionary algorithms. IEEE Trans Syst Man Cybern Syst 49(12):2416–2423

  20. Kohonen T (1998) The self-organizing map. Neurocomputing 21(1):1–6

  21. Kusner M, Sun Y, Kolkin N, Weinberger K (2015) From word embeddings to document distances. In: International Conference on Machine Learning, pp 957–966

  22. Liang G, He W, Xu C, Chen L, Zeng J (2015) Rumor identification in microblogging systems based on users behavior. IEEE Trans Comput Soc Syst 2(3):99–108

  23. Lu Y, Zhou J, Qin H, Li Y, Zhang Y (2010) An adaptive hybrid differential evolution algorithm for dynamic economic dispatch with valve-point effects. Expert Syst Appl 37(7):4842–4849

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

  25. Madisetty S, Desarkar MS (2018) A neural network-based ensemble approach for spam detection in twitter. IEEE Trans Comput Soc Syst 5(4):973–984

  26. Mallipeddi R, Suganthan PN, Pan QK, Tasgetiren MF (2011) Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl Soft Comput 11(2):1679–1696

  27. Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv:13013781

  28. Mishra S, Mondal S, Saha S, Coello CAC (2018) Gbos: Generalized best order sort algorithm for non-dominated sorting. Swarm Evol Comput 43:244–264

  29. Nenkova A, Vanderwende L (2005) The impact of frequency on summarization. Microsoft Research. Redmond, Washington, Tech Rep MSR-TR-2005 101

  30. Qian X, Li M, Ren Y, Jiang S (2019) Social media based event summarization by user–text–image co-clustering. Knowl-Based Syst 164:107–121

  31. Qu Y, Huang C, Zhang P, Zhang J (2011) Microblogging after a major disaster in china: a case study of the 2010 yushu earthquake. In: Proceedings of the ACM 2011 conference on Computer supported cooperative work. ACM, pp 25–34

  32. Radev DR, Jing H, Styś M, Tam D (2004) Centroid-based summarization of multiple documents. Inf Process Manag 40(6):919–938

  33. Robertson S, Zaragoza H, et al (2009) The probabilistic relevance framework: Bm25 and beyond. Found Trends®; Inf Retr 3(4):333–389

  34. Roussinov D, Chen H (1998) A scalable self-organizing map algorithm for textual classification: A neural network approach to thesaurus generation. Commun Cogn Artif Intell 15(1-2):81–111

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

  36. Rudra K, Ganguly N, Goyal P, Ghosh S (2018a) Extracting and summarizing situational information from the twitter social media during disasters. ACM Trans Web (TWEB) 12(3):17

  37. Rudra K, Goyal P, Ganguly N, Mitra P, Imran M (2018b) Identifying sub-events and summarizing disaster-related information from microblogs. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. ACM, pp 265–274

  38. Rudra K, Sharma A, Ganguly N, Ghosh S (2018c) Characterizing and countering communal microblogs during disaster events. IEEE Trans Comput Soc Syst 5(2):403–417

  39. Saini N, Saha S, Bhattacharyya P (2019a) Automatic scientific document clustering using self-organized multi-objective differential evolution. Cogn Comput 11(2):271–293

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

  41. Saini N, Saha S, Chakraborty D, Bhattacharyya P (2019c) Extractive single document summarization using binary differential evolution: Optimization of different sentence quality measures. PloS one 14(11):e0223,477

  42. Saini N, Saha S, Jangra A, Bhattacharyya P (2019d) Extractive single document summarization using multi-objective optimization: Exploring self-organized differential evolution, grey wolf optimizer and water cycle algorithm. Knowl-Based Syst 164:45–67

  43. Saini N, Saha S, Tuteja H, Bhattacharyya P (2019e) Textual entailment based figure summarization for biomedical articles. ACM Trans Multimed Comput Commun Appl 16(1)

  44. Sakaki T, Okazaki M, Matsuo Y (2010) Earthquake shakes twitter users: real-time event detection by social sensors. In: Proceedings of the 19th international conference on World wide web. ACM, pp 851–860

  45. Sedhai S, Sun A (2018) Semi-supervised spam detection in twitter stream. IEEE Trans Comput Soc Sys 5(1):169–175

  46. Vieweg S, Hughes AL, Starbird K, Palen L (2010) Microblogging during two natural hazards events: what twitter may contribute to situational awareness. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, pp 1079–1088

  47. Wang BC, Li HX, Li JP, Wang Y (2018) Composite differential evolution for constrained evolutionary optimization. IEEE Trans Syst Man Cybern Syst (99):1–14

  48. Wang L, Fu X, Menhas MI, Fei M (2010) A modified binary differential evolution algorithm. In: Life System Modeling and Intelligent Computing. Springer, pp 49–57

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

  50. Wang Y, Cai Z, Zhang Q (2011) Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans Evol Comput 15(1):55–66

  51. Welch BL (1947) The generalization ofstudent‘s’ problem when several different population variances are involved. Biometrika 34(1/2):28–35

  52. Weskida M, Michalski R (2019) Finding influentials in social networks using evolutionary algorithm. J Comput Sci 31:77–85

  53. Zhang D, Wei B (2014) Comparison between differential evolution and particle swarm optimization algorithms. In: 2014 IEEE International Conference on Mechatronics and Automation (ICMA). IEEE, pp 239–244

  54. Zhang H, Zhou A, Song S, Zhang Q, Gao XZ, Zhang J (2016) A self-organizing multiobjective evolutionary algorithm. IEEE Trans Evol Comput 20(5):792–806. https://doi.org/10.1109/TEVC.2016.2521868

  55. Zhang Y, Gong DW, Ding Z (2012) A bare-bones multi-objective particle swarm optimization algorithm for environmental/economic dispatch. Inf Sci 192:213–227

  56. Zhang Y, Gong Dw, Gao Xz, Tian T, Sun Xy (2020) Binary differential evolution with self-learning for multi-objective feature selection. Inf Sci 507:67–85

  57. Zhou X, Wan X, Xiao J (2016) Cminer: opinion extraction and summarization for chinese microblogs. IEEE Trans Knowl Data Eng 28(7):1650–1663

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Acknowledgments

Dr. Sriparna Saha would like to acknowledge the support of SERB Women in Excellence Award-SB/WEA-08/2017 for conducting this research.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology, Patna, Bihar, India

    Sriparna Saha & Pushpak Bhattacharyya

  2. Technology Studies Department, Endicott College of International Studies, Woosong University, Daejeon, South Korea

    Naveen Saini

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  1. Naveen Saini
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Correspondence to Naveen Saini.

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Saini, N., Saha, S. & Bhattacharyya, P. Microblog summarization using self-adaptive multi-objective binary differential evolution. Appl Intell 52, 1686–1702 (2022). https://doi.org/10.1007/s10489-020-02178-1

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