Case-Based Reasoning with Deep Learning for a Hybrid Approach to Legal Text Summarization

Legal documents are known to be long and complicated, which makes it essentially impossible for legal practitioners and researchers to quickly identify and extract relevant information. Here, a hybrid approach is presented that outperforms prior extractive and abstractive baselines on both lexical overlap and domain-specific reasoning metrics, which uses Case-Based Reasoning (CBR) for legal texts and concrete deep learning techniques for summary representation, accurately and efficiently producing summaries. Using a larger dataset of 4,968 legal cases from Kaggle, a multi-stage transformer architecture was constructed on top of the general CBR retrieval model created before to produce brief summaries along with CBR for context comprehension. The system was evaluated on legal outcome prediction and coherence of summary, with results showing performance superior to existing extractive and abstractive methods and trained the proposed model until 98% accuracy of legal entities, along with 46% more coherent legal corpus (baseline-enhanced) than state-of-the-art methods, compared using ROUGE scores above previous types by 23%. This study presents a hybrid legal text summarization framework that integrates CBR with transformer-based models. Extensive experiments show superior performance over recent baselines, achieving higher factual accuracy, reasoning fidelity, and legal entity preservation.