This work proposes a multi-task Natural Language Processing (NLP) system to normalize and summarize the descriptions into a uniform structure. A dataset was curated from an official public database and broken into several constituent entities representing a particular aspect of the description. A model is trained on the annotated features independently and jointly to generate a simple and uniform summary. We also introduce our human metrics to judge the quality of the generated summary with respect to human comprehension and content accuracy.