The legal environment is hard to navigate as it involves large amount of unstructured data. Legal companies have typically handled and processed massive amounts of material manually in order to achieve this goal. Currently, a significant number of man-hours are required to do legal research, severely limiting legal professionals to deliver profit-making potential. Artificial intelligence-based application helps in balancing the whole legal fraternity in delivering services in less time. The number of information that legal practitioners must deal with has lately increased to the point that simply glancing through them would be time-consuming. Few artificial intelligence-based legal tools are used for purposes like document automation, contract review, legal research, and legal analytics and make legal document management easier. Among various artificial intelligence-based tools, summarizing tool can make things much easier for the legal practitioner as well. To assist legal practitioners, text summarization methodology has been proposed in this paper, which includes approaches, such as TF-IDF (term frequency-inverse document frequency) and a few additional NLP tools. When applied to legal papers annotated by legal professionals, the proposed technique achieved accuracy greater than 85% on comparing the summarized result with the annotated documents. This paper also includes a comparison and analysis of the performance of a few additional online tools available in order to find out the best tool to solve the purpose and to choose the most efficient technique.