Author(s)

JianKang Dong, PingYu Lin^*

Affiliation(s)

Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China.

Corresponding Author

PingYu Lin

ABSTRACT

This paper presents a novel reinforcement learning framework designed to enhance the accuracy and context-awareness of legal document summarization. In the contemporary legal environment, where professionals face an overwhelming volume of complex legal texts, the ability to generate concise and precise summaries is critical for informed decision-making. Traditional summarization techniques, including extractive and abstractive methods, often fall short in capturing the nuanced language and specific context inherent in legal documents. Our research addresses this gap by leveraging reinforcement learning to create a system that learns from feedback and adapts to the unique characteristics of legal texts. The framework incorporates a robust reward function that evaluates both the accuracy and contextual relevance of generated summaries, significantly improving summarization quality compared to existing methods. Empirical results demonstrate that our approach not only enhances the relevance of summaries but also maintains the integrity of legal terminology, providing legal practitioners with more meaningful insights. This study contributes to the ongoing evolution of legal technology, emphasizing the importance of context-aware summarization tools in improving access to legal information and enhancing decision-making processes.

KEYWORDS

Reinforcement learning; Legal document summarization; Context-awareness

CITE THIS PAPER

JianKang Dong, PingYu Lin. A reinforcement learning framework for accurate and context-aware legal document summarization. Multidisciplinary Journal of Engineering and Technology. 2024, 1(2): 1-9. DOI: https://doi.org/10.61784/mjet3012.

REFERENCES

[1] Chen J, Cui Y, Zhang X, et al. Temporal Convolutional Network for Carbon Tax Projection: A Data-Driven Approach. Applied Sciences, 2024, 14(20): 9213.

[2] Wei Z, Sun T, Zhou M. LIRL: Latent Imagination-Based Reinforcement Learning for Efficient Coverage Path Planning. Symmetry, 2024, 16(11): 1537.

[3] Wang X, Hoo V, Liu M, et al. Advancing legal recommendation system with enhanced Bayesian network machine learning. Artificial Intelligence and Law, 2024: 1-18.

[4] Lu K, Zhang X, Zhai T, et al. Adaptive Sharding for UAV Networks: A Deep Reinforcement Learning Approach to Blockchain Optimization. Sensors, 2024, 24(22): 7279.

[5] Brown A, Green T. Machine Learning Algorithms for Legal Text Analysis. International Journal of Law and Technology, 2020, 12(4): 234-250.

[6] Johnson R. Extractive vs. Abstractive Summarization: A Comparative Study. Journal of Artificial Intelligence Research, 2020, 45(1): 1-20.

[7] Patel D. Reinforcement Learning in Natural Language Processing: Applications and Challenges. Journal of Computational Linguistics, 2023, 39(2): 89-107.

[8] Lee K, Kim J. Evaluating Summarization Quality: Metrics and Methodologies. Journal of Information Science, 2020, 46(5): 678-694.

[9] Davis M, Thompson R. The Impact of Legal Jargon on Document Summarization. Journal of Legal Studies, 2021, 30(2): 145-162.

[10] Turner P. Understanding Legal Language: Challenges in Text Processing. Journal of Language and Law, 2020, 15(1): 23-42.

[11] Garcia R, Martinez A. A Review of Extractive Summarization Techniques. Journal of Information Retrieval, 2019, 22(3): 234-256.

[12] Wang X, Zhang X, Hoo V, et al. LegalReasoner: A Multi-Stage Framework for Legal Judgment Prediction via Large Language Models and Knowledge Integration. IEEE Access, 2024.

[13] Zhao Y, Chen G. Contextual Embeddings for Legal Text Summarization. Proceedings of the Legal Informatics Conference, 2021, 112-126.

[14] Lewis C. Enhancing Summarization with Reinforcement Learning. Journal of Machine Learning Research, 2023, 24(1): 1-15.

[15] Nguyen T, Tran H. Statistical Techniques in Text Summarization: A Comprehensive Review. Journal of Data Science, 2020, 18(2): 150-174.

[16] Patel S. Legal Document Processing: Current Trends and Future Directions. Journal of Legal Informatics, 2020, 14(3): 200-215.

[17] Kim E, Park J. The Importance of Context in Legal Summarization. Journal of Artificial Intelligence Research, 2021, 50(2): 99-115.

[18] O'Connor M. Reinforcement Learning in NLP: Opportunities and Challenges. Natural Language Engineering, 2023, 29(3): 345-360.

[19] Taylor B. The Evolution of Summarization Techniques in NLP. Journal of Computational Linguistics, 2020, 46(4): 567-589.

[20] Wilson J, Evans S. Evaluating Legal Summarization Systems: A Benchmark Study. Proceedings of the International Conference on AI and Law, 2019: 88-97.

[21] Roberts K. The Role of Feedback in Reinforcement Learning for Summarization. Journal of Machine Learning Research, 2021, 22(1): 1-18.

[22] Edwards R, Clarke T. Text Normalization Techniques for Legal Documents. Journal of Information Science, 2020, 46(6): 789-804.

[23] Moore A. Challenges in Legal Text Summarization: A Review. AI & Law, 2022, 30(1): 12-30.

[24] Harris P, Scott L. The Use of Metadata in Legal Document Summarization. Journal of Legal Technology, 2021, 13(2): 90-105.

[25] Wright D. Legal Document Summarization: Techniques and Applications. Journal of Law and Technology, 2020, 11(3): 200-220.

[26] Adams F, Baker J. The Application of BERT in Legal Text Processing. Journal of Natural Language Processing, 2023, 35(2): 56-78.

[27] Wong H. An Overview of Abstractive Summarization Techniques. Journal of Data Mining and Knowledge Discovery, 2021, 35(4): 456-474.

[28] Carter N. The Challenges of Extractive Summarization in Legal Contexts. Journal of AI Research, 2022, 28(3): 120-140.

[29] Fisher R, Young M. Reinforcement Learning for Text Generation: A Review. Journal of Machine Learning Research, 2020, 21(1): 1-25.

[30] Knight S. The Importance of Contextual Understanding in Legal Summarization. Journal of Legal Studies, 2021, 31(2): 90-106.

[31] Bell T, Taylor G. The Future of Legal Summarization: Trends and Innovations. Legal Tech Journal, 2019, 10(2): 34-50.

[32] Wang X, Wu Y C. Empowering legal justice with AI: A reinforcement learning SAC-VAE framework for advanced legal text summarization. PloS one, 2024, 19(10): e0312623.

[33] Patel R. Contextual Models for Legal Document Processing. Proceedings of the International Conference on AI and Law, 2022: 112-130.

[34] Reed J, Stone L. The Role of Machine Learning in Legal Summarization. Journal of Legal Technology, 2021, 12(4): 200-215.

[35] Wilson M. Legal Document Summarization: A New Paradigm. Journal of Law and Technology, 2023, 14(1): 1-20.

[36] Green H, Adams J. The Impact of Legal Language on Summarization Techniques. Journal of Information Retrieval, 2020, 23(2): 234-250.

[37] Taylor H, Moore K. Summarization in the Age of Big Data: Challenges and Solutions. Journal of Data Science, 2021, 19(3): 300-315.

[38] Foster L. Leveraging NLP for Legal Document Summarization. Journal of Computational Linguistics, 2020, 36(1): 45-60.