Unsupervised Extractive Opinion Summarization Using Sparse Coding

TL;DR

This paper introduces SemAE, an unsupervised semantic autoencoder that uses dictionary learning to generate extractive, aspect-specific opinion summaries from multiple reviews, demonstrating strong results on benchmark datasets.

Contribution

The paper proposes SemAE, a novel unsupervised model that captures semantic information for extractive opinion summarization and enables controllable, aspect-specific summaries.

Findings

Strong performance on SPACE and AMAZON datasets

Effective capture of semantic concepts via dictionary learning

Ability to generate aspect-specific summaries

Abstract

Opinion summarization is the task of automatically generating summaries that encapsulate information from multiple user reviews. We present Semantic Autoencoder (SemAE) to perform extractive opinion summarization in an unsupervised manner. SemAE uses dictionary learning to implicitly capture semantic information from the review and learns a latent representation of each sentence over semantic units. A semantic unit is supposed to capture an abstract semantic concept. Our extractive summarization algorithm leverages the representations to identify representative opinions among hundreds of reviews. SemAE is also able to perform controllable summarization to generate aspect-specific summaries. We report strong performance on SPACE and AMAZON datasets, and perform experiments to investigate the functioning of our model. Our code is publicly available at https://github.com/brcsomnath/SemAE.