Analysis of Effective Approaches for Legal Texts Summarization Using Deep Learning

Authors

  • Ms. Shruti J. Sapra (Thakur)  Assistant Professor, Department of CSE, G H Raisoni College of Engineering, Nagpur, Maharashtra, India
  • Dr. Avinash S. Kapse  HOD, Information Technology, Anuradha Engineering College Chikhli, Maharashtra, India

Keywords:

Deep learning algorithms, legal text summarization, Abstractive text summarization, strategies for summarization.

Abstract

Now a day there is a great importance is associated with the text summarization as there is a large amount of data is available on the different social media platforms, websites and blogs. Also there are large number of tools used for the summarization of the text available in the different forms this text which is available from the different sources are present in the various format some of the data is in the structured and some are in the unstructured format. The user or the application which is using this data has to be careful about the use of the data as the data is available has to be authentic and must be delivered to the user in the proper format. The large amount of time is associated with understanding the meaning of the data. Therefore user has to invest large amount of time in reading the data in other words there is a great deal in getting the meaning of the data as the data is in large amount and hence getting the meaning from such a huge amount of data is not a simple job. The best option is to summarize means to shorten the data in so that user will require less amount of time in understanding the meaning of the data and will be useful for the user or the application in which it is used. This paper essentially discusses the various approaches from the domain of the deep learning and machine learning in shortening the data as well as summarizing the data. Different algorithms are also discussed in order to understand the various parameters associated with the text summarization. Also the best algorithm must be chosen for the effective text summarization process for the better results and better efficiency. Also in India there is a great importance is associated with the legal text but many a times the common man or the user is unable to recognize the meaning of the text by means of the text is very large and descriptive hence it is very essential to decode such a text in simple and short text. Thus the specialized algorithms are used to interpret the meaning or directly the text in to the format the use is able to understand. In other words the techniques have been discussed shortens or summarizes the legal text and makes easier or readable for the application or the user.

References

  1. A full Python Implementation of the ROUGE Metric (not a wrapper). Retrieved from https://github.com/pltrdy/rouge/.
  2. Alguliev, R., Aliguliyev, R., 2007. Experimental investigating the F-measure as similarity measure for automatic text summarization. Appl. Comput. Math. 6 (2), 278–287.
  3. Allahyari, M., Pouriyeh, S., Assefi, M., Safaei, S., Trippe, E.D., Gutierrez, J.B., Kochut, K., 2017. Text summarization techniques: a brief survey. arXiv preprint arXiv:1707.02268.
  4. Chen, Y.C., Bansal, M., 2018. Fast abstractive summarization with reinforce-selected sentence rewriting. arXiv preprint arXiv:1805.11080.
  5. Conneau, A., Kiela, D., Schwenk, H., Barrault, L., Bordes, A., 2017. Supervised learning of universal sentence representations from natural language inference data. arXiv preprint arXiv:1705.02364.
  6. General purpose unsupervised sentence representations. Retrieved from https:// github.com/epfml/sent2vec.
  7. Infersent Sentence Embedding. Retrieved from https://github.com/facebook research/InferSent
  8. Kanapala, A., Pal, S., Pamula, R., 2019. Text summarization from legal documents: a survey. Artif. Intell. Rev. 51 (3), 371–402.
  9. Kasture, N.R., Yargal, N., Singh, N.N., Kulkarni, N., Mathur, V., 2014. A survey on methods of abstractive text summarization. Int. J. Res. Merg. Sci. Technol 1 (6), 53–57.
  10. Kavila, S.D., Puli, V., Raju, G.P., Bandaru, R., 2013. An automatic legal document summarization and search using hybrid system. In: Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA). Springer, Berlin, Heidelberg, pp. 229–236.
  11. Keras: The Python Deep Learning library. Retrieved from https://keras.io/.Kim, M.Y., Xu, Y., Goebel, R., 2012. Summarization of legal texts with high cohesion and automatic compression rate. In: JSAI International Symposium on Artificial Intelligence. Springer, Berlin, Heidelberg, pp. 190–204.
  12. Lin, C.Y., 2004. Rouge: A package for automatic evaluation of summaries. In Text summarization branches out. pp. 74–81.
  13. Tom M. Mitchell, Herbert Jones, “Machine learning”, Indian edition. Tata Mc Graw Hill Publications, 1 July 2017.
  14. SukritiVerma,VagishaNidhi,"ExtractiveSummarizationusingDeepLearnin g",arXiv:1708.00439v2[cs .CL], 9 Jan 2019.
  15. Bhumika, V. Srikanth,” Abstractive Summarization with NLP Makes Easier to Memorize”, International Journal of Recent Technology and Engineering (IJRTE), ISSN: 2277-3878, Volume-8 Issue-5, Jan 2020.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2021-06-30

Issue

Volume 8, Issue 4, May-June-2021

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Ms. Shruti J. Sapra (Thakur), Dr. Avinash S. Kapse, " Analysis of Effective Approaches for Legal Texts Summarization Using Deep Learning" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 4, pp.53-59, May-June-2021.