The ever-increasing volume of information on social media platforms poses challenges for individuals to stay updated on current events, particularly those that may not receive widespread media coverage. When a controversial or major event happens in one corner of the world, the public only becomes aware of it on any celebrity posting about it or the media reporting it. By the time a celebrity tweets about an incident, the story would have progressed and started to involve multiple actors on the scene. To address this issue, there is a need for an automated summarization system that can effectively analyze and condense the content of related Twitter and Reddit posts. This paper presents a survey on looking into the various Natural Language Processing (NLP) techniques, data collection and preprocessing techniques, and already existing advanced algorithms. The basic methodology involves data collection from APIs and making use of web scraping tools, followed by preprocessing and analysis to filter out noise and irrelevant information. Various NLP techniques, such as topic modeling, sentiment analysis, and word embedding models are looked into for the application of identifying key themes and opinions expressed in the posts. Hence, this survey addresses the need for efficient and accurate summarization of social media content, providing valuable insights and timely updates on current events.