Summarization of legal judgments using gravitational search algorithm

  • Ambedkar KanapalaEmail author
  • Srikanth Jannu
  • Rajendra Pamula
Original Article


Text summarization is an extraction of important text from the original document. The objective of any automatic text summarization system, especially in legal domain, is to produce a summary which is close to human-generated summaries. In this article, we present the summarization of legal documents as binary optimization problem where fitness of the solution is derived based on the weighting of individual statistical features of each sentence such as length of the sentence, sentence position, degree of similarity, term frequency–inverse sentence frequency and keywords to generate summary of the document. In this paper, a gravitational search algorithm is adopted that works on the basis of the law of gravity to optimize the summary of the document. To show the efficacy of the proposed method, we compare the experimental results with particle swarm optimization, genetic algorithm, TextRank, latent semantic analysis, MEAD, MS-Word, SumBasic using ROUGE evaluation metrics on the FIRE-2014 data set. The experimental results of the proposed method show better than the existing state-of-the-art methods in terms of various performance metrics.


Legal summarization Heuristic search algorithms Gravitational search algorithm PSO Genetic algorithm 


Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.


  1. 1.
    Al-Betar MA, Awadallah MA, Faris H, Yang XS, Khader AT, Alomari OA (2018) Bat-inspired algorithms with natural selection mechanisms for global optimization. Neurocomputing 273:448–465CrossRefGoogle Scholar
  2. 2.
    Aljarah I, Faris H, Mirjalili S (2018) Optimizing connection weights in neural networks using the whale optimization algorithm. Soft Comput 22(1):1–15CrossRefGoogle Scholar
  3. 3.
    Issa M, Hassanien AE, Oliva D, Helmi A, Ziedan I, Alzohairy A (2018) ASCA-PSO: adaptive sine cosine optimization algorithm integrated with particle swarm for pairwise local sequence alignment. Expert Syst Appl 99:56–70CrossRefGoogle Scholar
  4. 4.
    Mokarram V, Banan MR (2018) A new pso-based algorithm for multi-objective optimization with continuous and discrete design variables. Struct Multidiscip Optim 57(2):509–533MathSciNetCrossRefGoogle Scholar
  5. 5.
    Tayal MA, Raghuwanshi MM, Malik LG (2017) Atssc: development of an approach based on soft computing for text summarization. Comput Speech Lang 41:214–235CrossRefGoogle Scholar
  6. 6.
    Touil DE, Terki N, Medouakh S (2017) Learning spatially correlation filters based on convolutional features via PSO algorithm and two combined color spaces for visual tracking. Appl Intell 48:1–10Google Scholar
  7. 7.
    Al-Radaideh QA, Bataineh DQ (2018) A hybrid approach for arabic text summarization using domain knowledge and genetic algorithms. Cogn Comput 10:1–19CrossRefGoogle Scholar
  8. 8.
    Mosa MA, Hamouda A, Marei M (2017) Ant colony heuristic for user-contributed comments summarization. Knowl Based Syst 118:105–114CrossRefGoogle Scholar
  9. 9.
    Mosa MA, Hamouda A, Marei M (2017) Graph coloring and aco based summarization for social networks. Expert Syst Appl 74:115–126CrossRefGoogle Scholar
  10. 10.
    Al-Abdallah RZ, Al-Taani AT (2017) Arabic single-document text summarization using particle swarm optimization algorithm. Proc Comput Sci 117:30–37CrossRefGoogle Scholar
  11. 11.
    Ismkhan H (2018) Black box optimization using evolutionary algorithm with novel selection and replacement strategies based on similarity between solutions. Appl Soft Comput 64:260–271CrossRefGoogle Scholar
  12. 12.
    Zhao Y, Cai Y, Cheng D (2017) A novel local exploitation scheme for conditionally breeding real-coded genetic algorithm. Multimed Tools Appl 76(17):17955–17969CrossRefGoogle Scholar
  13. 13.
    Saravanan M, Ravindran B, Raman S (2006) Improving legal document summarization using graphical models. Front Artif Intell Appl 152:51Google Scholar
  14. 14.
    Chieze E, Farzindar A, Lapalme G (2010) An automatic system for summarization and information extraction of legal information. Semant Process Leg Texts 6036:216–234CrossRefGoogle Scholar
  15. 15.
    Farzindar A, Lapalme G (2004) Legal text summarization by exploration of the thematic structures and argumentative roles. In: Text summarization branches out workshop held in conjunction with ACL, pp 27–34Google Scholar
  16. 16.
    Hachey B, Grover C (2006) Extractive summarisation of legal texts. Artif Intell Law 14(4):305–345CrossRefGoogle Scholar
  17. 17.
    Galgani F, Compton P, Hoffmann A (2012) Combining different summarization techniques for legal text. In: Proceedings of the workshop on innovative hybrid approaches to the processing of textual data. Association for Computational Linguistics, pp 115–123Google Scholar
  18. 18.
    Compton P, Jansen R (1990) Knowledge in context: a strategy for expert system maintenance. In: Proceedings of the 2nd Australian joint conference on artificial intelligence. Springer, New York, pp 292–306Google Scholar
  19. 19.
    Kim MY, Xu Y, Goebel R (2013) Summarization of legal texts with high cohesion and automatic compression rate. New Front Artif Intell 2013:190–204CrossRefGoogle Scholar
  20. 20.
    Galgani F, Compton P, Hoffmann A (2014) Hauss: incrementally building a summarizer combining multiple techniques. Int J Hum Comput Stud 72(7):584–605CrossRefGoogle Scholar
  21. 21.
    Polsley S, Jhunjhunwala P, Huang R (2016) Casesummarizer: a system for automated summarization of legal texts. In: Proceedings of COLING 2016, the 26th international conference on computational linguistics: system demonstrations, pp 258–262Google Scholar
  22. 22.
    Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248CrossRefzbMATHGoogle Scholar
  23. 23.
    Gupta V, Chauhan P, Garg S, Borude A, Krishnan S (2012) An statistical tool for multi-document summarization. Int J Sci Res Publ 2(5):1–5Google Scholar
  24. 24.
    Lin CY (2004) Rouge: a package for automatic evaluation of summaries. In: Text summarization branches out: proceedings of the ACL-04 workshop, vol 8, BarcelonaGoogle Scholar
  25. 25.
    Vanderwende L, Suzuki H, Brockett C, Nenkova A (2007) Beyond sumbasic: task-focused summarization with sentence simplification and lexical expansion. Inf Process Manag 43(6):1606–1618CrossRefGoogle Scholar
  26. 26.
    Radev DR, Allison T, Blair-Goldensohn S, Blitzer J, Celebi A, Dimitrov S, Drabek E, Hakim A, Lam W, Liu D et al (2004) Mead—a platform for multidocument multilingual text summarization. In: LRECGoogle Scholar
  27. 27.
    Steinberger J, Jezek K (2004) Using latent semantic analysis in text summarization and summary evaluation. Proc ISIM 4:93–100Google Scholar
  28. 28.
    Mihalcea R, Tarau P (2004) Textrank: bringing order into text. EMNLP 4:404–411Google Scholar
  29. 29.
    Tang KS, Man KF, Kwong S, He Q (1996) Genetic algorithms and their applications. IEEE Signal Process Mag 13(6):22–37CrossRefGoogle Scholar
  30. 30.
    Kennedy J, Optimization REPS (1995). In: IEEE international conference on neural networks, vol 4Google Scholar
  31. 31.
    Aliguliyev RM (2009) A new sentence similarity measure and sentence based extractive technique for automatic text summarization. Expert Syst Appl 36(4):7764–7772CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Ambedkar Kanapala
    • 1
    Email author
  • Srikanth Jannu
    • 2
  • Rajendra Pamula
    • 1
  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology (ISM)DhanbadIndia
  2. 2.Department of Computer Science and EngineeringVaagdevi Engineering CollegeWarangalIndia

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