A LSTM based Deep Learning Model for Text Summarization

As different users provide different reviews for a product/service, it has become increasingly difficult for common people to understand the customer reviews found on various apps or websites. People are sometimes too lazy to read reviews on various subjects all the way through before making a judgement, despite the fact that they can take time. Even if they wanted to, people cannot read every line of a review. As a result, a text summary model would greatly simplify this process. The purpose of a text summary is to draw out the most significant data from a long document and leave out any that are superfluous or uninteresting. This text summarizer will automatically produce a useful summary from reviews using LSTM. Sentences from the input text will be separated and converted into vectors. A material summary is a process of reducing a large body of text while preserving its original context. The summary should read easily. In this project, our goal is to create a model that accepts reviews of foods as input and outputs a summary of the review. This helps the people who are ordering the food if they want to know about the food that they are looking for.