MRCBert: A Machine Reading ComprehensionApproach for Unsupervised Summarization

05/01/2021
by   Saurabh Jain, et al.
0

When making an online purchase, it becomes important for the customer to read the product reviews carefully and make a decision based on that. However, reviews can be lengthy, may contain repeated, or sometimes irrelevant information that does not help in decision making. In this paper, we introduce MRCBert, a novel unsupervised method to generate summaries from product reviews. We leverage Machine Reading Comprehension, i.e. MRC, approach to extract relevant opinions and generate both rating-wise and aspect-wise summaries from reviews. Through MRCBert we show that we can obtain reasonable performance using existing models and transfer learning, which can be useful for learning under limited or low resource scenarios. We demonstrated our results on reviews of a product from the Electronics category in the Amazon Reviews dataset. Our approach is unsupervised as it does not require any domain-specific dataset, such as the product review dataset, for training or fine-tuning. Instead, we have used SQuAD v1.1 dataset only to fine-tune BERT for the MRC task. Since MRCBert does not require a task-specific dataset, it can be easily adapted and used in other domains.

READ FULL TEXT
research
05/30/2020

Topic Detection and Summarization of User Reviews

A massive amount of reviews are generated daily from various platforms. ...
research
05/04/2022

Efficient Few-Shot Fine-Tuning for Opinion Summarization

Abstractive summarization models are typically pre-trained on large amou...
research
04/14/2021

Mitigating the Effects of Reading Interruptions by Providing Reviews and Previews

As reading on mobile devices is becoming more ubiquitous, content is con...
research
04/18/2017

Mining Worse and Better Opinions. Unsupervised and Agnostic Aggregation of Online Reviews

In this paper, we propose a novel approach for aggregating online review...
research
12/04/2016

CER: Complementary Entity Recognition via Knowledge Expansion on Large Unlabeled Product Reviews

Product reviews contain a lot of useful information about product featur...
research
07/13/2022

Developing a Component Comment Extractor from Product Reviews on E-Commerce Sites

Consumers often read product reviews to inform their buying decision, as...
research
04/03/2019

BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis

Question-answering plays an important role in e-commerce as it allows po...

Please sign up or login with your details

Forgot password? Click here to reset