Nowadays, YouTube has wide variety of videos that includes videos related to educational lectures and entertainment. Sometimes it is very difficult to get relevant information from videos with long duration and consumes more user time. It can also lead to user dissatisfaction. To address this problem, we came up with a solution of Video Transcript Condenser System. Our project aim is to retrieve key points from the YouTube videos instead of watching the video till end using natural language processing and machine learning algorithms. The system can take more than one video at a time and generate summaries. The system gets the transcripts from YouTube videos, and then these are summarized to get key points. The key points generated ensures that they contain the video’s whole information without leaving any single point so that there is no loss of information. The extracted summaries are easy to understand so that users like students and researchers can quickly grasp the information from the video without watching the complete video which saves time and effort. With the help of the system, users will not miss any key point. The system has user-centric features: The summary length can be chosen by the user, specified keywords by the user are highlighted in the summary, the summaries can be downloaded, summary’s polarity is given by the system, text-to-speech feature allows summaries to be read aloud, and the audio files can be downloaded to access them offline.