OpinionDigest: A Simple Framework for Opinion Summarization

TL;DR

OpinionDigest is a novel framework for opinion summarization that uses aspect-based sentiment analysis and a Transformer model to generate customizable, abstractive summaries without relying on gold-standard training data.

Contribution

It introduces a simple, training-free approach combining opinion extraction and a Transformer for abstractive summarization, enabling customization and outperforming baselines.

Findings

Outperforms competitive baselines on Yelp data

Produces informative and customizable summaries

Verifies effectiveness through human studies

Abstract

We present OpinionDigest, an abstractive opinion summarization framework, which does not rely on gold-standard summaries for training. The framework uses an Aspect-based Sentiment Analysis model to extract opinion phrases from reviews, and trains a Transformer model to reconstruct the original reviews from these extractions. At summarization time, we merge extractions from multiple reviews and select the most popular ones. The selected opinions are used as input to the trained Transformer model, which verbalizes them into an opinion summary. OpinionDigest can also generate customized summaries, tailored to specific user needs, by filtering the selected opinions according to their aspect and/or sentiment. Automatic evaluation on Yelp data shows that our framework outperforms competitive baselines. Human studies on two corpora verify that OpinionDigest produces informative summaries and shows promising customization capabilities.