Generating Summaries of Customer Reviews: A Hybrid Model Approach

Online customer reviews provide researchers with the knowledge of what people think of products and services. It quickly becomes evident that it would be tedious to manually summarize all the reviews since the data is inherently unstructured. The paper suggests an automatic review sum-marization system framework through TensorFlow, leveraging advanced deep learning. The technique uses two models: RNN-LSTM(LSTM) and CopyCat. LSTM is used widely for long reviews because of its ability to grasp the idea and the attitude of the writer, ensuring that summaries convey a sentiment genuinely felt by the reviewer. Then, the task is passed to CopyCat, which utilizes several informative words for the person's annotation. Thus, the results benefit from additional context words or facts. By leveraging the strengths of these models, the system can provide concise yet meaningful summaries that reflect the true sentiments expressed in the original reviews. Additionally, this work trains the models separately on a handpicked dataset of Amazon reviews. By doing this, they learned the specific language and words used when discussing these products. This custom training enabled the models to create summaries that accurately reflect customer opinions about the items.