A SURVEY ON SUMMARIZATION OF LEGAL TEXTS

The task of summarizing legal texts is of paramount importance in facilitating access to legal knowledge. This survey paper critically reviews and analyzes diverse methodologies applied to the challenging task of summarizing legal texts. In the supervised realm, models such as GIST, SummaRuNNer, and BERTSUM have gained prominence, demonstrating prowess in extractive summarization techniques. Semi-supervised methods, leveraging innovative techniques such as TF-IDF Scores and Sentence Embeddings, have proven effective in handling the intricate structure of court judgments. Unsolicited approaches, CaseSummarizer and exemplified by USLT-LEGAL-BERT, address the challenges of summarization without the luxury of labeled data, showcasing their adaptability to diverse legal domains. Furthermore, the paper explores legal document retrieval strategies, incorporating document vector embeddings and deep learning techniques, underscoring the significance of domain-specific approaches. The study encompasses a variety of models, from Legal-BERT to hierarchical encoder decoder frameworks, showcasing their applicability in handling the complexity inherent in legal texts. Additionally, the survey delves into BERT-based techniques for legal classification tasks, emphasizing the crucial role of context and introducing innovative methods such as “Stride 64.” In the realm of legal text simplification, the survey scrutinizes the KIS model and graph-based approaches, revealing nuanced trade-offs between readability, fluency, and meaning preservation. The research culminates with an exploration of the team’s participation in LegalEval 2023 shared tasks, underscoring the prowess of deep learning models in tasks such as rhetorical role classification and legal named entity extraction.This survey paper provides a nuanced and comprehensive perspective on the evolving landscape of legal document summarization, offering valuable insights into current methodologies, challenges, and potential future directions for advancing the field.