ProdRev: A DNN framework for empowering customers using generative pre-trained transformers

TL;DR

ProdRev leverages fine-tuned generative pre-trained transformers to summarize product reviews with common sense, aiding customers in making quicker, more informed decisions amidst overwhelming review data.

Contribution

This paper introduces a novel framework that uses a fine-tuned GPT-3 model for abstractive review summarization incorporating common sense, enhancing decision-making support for e-commerce customers.

Findings

Effective abstractive summarization of reviews

Incorporation of common sense improves decision relevance

Provides pros and cons for user decision support

Abstract

Following the pandemic, customers, preference for using e-commerce has accelerated. Since much information is available in multiple reviews (sometimes running in thousands) for a single product, it can create decision paralysis for the buyer. This scenario disempowers the consumer, who cannot be expected to go over so many reviews since its time consuming and can confuse them. Various commercial tools are available, that use a scoring mechanism to arrive at an adjusted score. It can alert the user to potential review manipulations. This paper proposes a framework that fine-tunes a generative pre-trained transformer to understand these reviews better. Furthermore, using "common-sense" to make better decisions. These models have more than 13 billion parameters. To fine-tune the model for our requirement, we use the curie engine from generative pre-trained transformer (GPT3). By using generative models, we are introducing abstractive summarization. Instead of using a simple extractive method of summarizing the reviews. This brings out the true relationship between the reviews and not simply copy-paste. This introduces an element of "common sense" for the user and helps them to quickly make the right decisions. The user is provided the pros and cons of the processed reviews. Thus the user/customer can take their own decisions.