Survey on the legal question answering problem

Recent advances in multi-document summarization in the legal domain have demonstrated significant progress in the extraction and compression of information from legal texts. Current methods utilize a combination of natural language processing, machine learning, and data mining techniques to identify and distill key elements and themes from a multitude of legal documents. This process creates structured, concise, and relevant summaries based on specific legal queries or topics, often referred to as multi-document abstracts. These abstracts facilitate a more efficient review by capturing the essence of complex and voluminous legal materials without losing the necessary detail. The focus of recent research has been on enhancing the accuracy of information retrieval, improving the coherence of the generated summaries, and ensuring the relevacy of the content to the specific legal issue at hand. Although challenges remain, particularly in the nuances of legal language and the diversity of document types, the trajectory of the field is toward more sophisticated and user-friendly systems that promise to transform the landscape of legal research and information accessibility.