Numerous products are eligible for reviews on websites like Flipkart and Amazon.com . The amount of consumer reviews that a product receives increases along with the popularity of e-commerce. There may be hundreds of thousands of evaluations for a single product; some may be repetitive. For this reason, computerized review summarization has a lot of potential to aid consumers in making quick decisions regarding particular goods. Due to the fact that a single producer may sell a variety of goods. Manufacturers should keep track of client feedback and opinions. Review summarizing is the technique of constructing a summary from review sentences. Provided an item review, a condensed form of the review is produced with the emotions and points kept intact. Additionally, the emotion or tone of the review will be established, and a summary of typical positive and unfavorable item reviews will be provided. To summarize, neural networks like the recurrent neural network (RNN) and the Natural Language Processing Toolkit are employed. The Seq2Seq model, encoder-decoder architecture, is integrated with the RNN architecture.