LegalSummNet: A Transformer-Based Model for Effective Legal Case Summarization

Expanding legal documents has become increasingly complicated and presents a greater challenge to legal professionals in extracting relevant information efficiently. In this paper, a new two-stage hybrid summarization system, called LegalSummNet, is introduced. It excels in handling the peculiarities of legal texts, such as their extremely long length, complex syntax, and specialized vocabulary. LegalSummNet combines an extractive filtering model with an attention-weighted filtering module and a transformer-based abstractive generation model, enabling it to identify significant elements and produce compact, coherent, and semantically competent summaries. The proposed model is tested using a large-scale dataset comprising a legal case and shows significant improvements compared to robust baselines, such as BERTSumExt and LegalT5, in performance measured by ROUGE-1, ROUGE-2, ROUGE-L, and BERT Score. A greater compression efficiency is also evident with the model. Hence, there is a significant application of real-world systems in generating case briefs and summaries related to contracts. The findings demonstrate that LegalSummNet is effective in enhancing the accessibility of legal documents and supporting informed decision-making.