The rapid increase in textual content generation from sources like WhatsApp, Instagram, and Amazon produces massive amounts of data daily. Interpreting this data can help business owners understand the public perception of their products or services and make informed decisions. Due to the large volume of text, Natural Language Processing (NLP), a crucial aspect of Sentiment Analysis (SA), is essential for content interpretation. This research focuses on developing a Consumer Review Summarization (CRS) model using NLP techniques and Long Short-Term Memory (LSTM) to summarize data and provide businesses with significant insights into consumer behavior and preferences. The CRS model's effectiveness relies on the SA model and consists of two phases: SADL and CRS. The SADL phase includes review preprocessing, feature extraction, and sentiment classification, while the CRS phase performs automatic summarization based on the SADL outcomes.