CaseBrief AI: Advanced Legal Summarization Explainability Engine

Legal text analysis automation requires performing two essential operations that include role labeling and legal document summarization. Complex legal language surpasses standard methods in its ability to process both diversity and complexity. The authors propose a new methodology that unites XGBoost for role identification as well as hybrid summarization techniques with an optimized legal document model built using Legal-BERT. The proposed method commences with Legal-BERT to detect legal entities. The proposed summarization system merges extractive and abstractive methods for creating logical and concise summaries after role annotation has occurred. The model adopts SHAP and LIME XAI techniques for its decisionmaking process, which provides transparency and interpretability in its operations. The model demonstrates its effectiveness through experimental results done on extensive legal data, which exceeded basal methods during role tagging and summarization processes. The model's resistance is strengthened through the implementation of XGBoost, and it delivers intelligible explanations for its output using XAI techniques. This method enables legal professionals to obtain powerful tools that both enhance AI decision transparency and minimize the time needed for document analysis.