Weakly-Supervised Opinion Summarization by Leveraging External Information

TL;DR

This paper introduces AspMem, a weakly-supervised opinion summarization method that leverages external domain knowledge through memory cells to improve aspect identification and opinion summarization without requiring manual annotations.

Contribution

The paper presents AspMem, a novel generative approach using memory cells to incorporate external knowledge, enhancing opinion summarization without human supervision.

Findings

AspMem outperforms state-of-the-art methods in aspect identification.

AspMem achieves superior opinion summarization results.

The method reduces reliance on handcrafted annotations.

Abstract

Opinion summarization from online product reviews is a challenging task, which involves identifying opinions related to various aspects of the product being reviewed. While previous works require additional human effort to identify relevant aspects, we instead apply domain knowledge from external sources to automatically achieve the same goal. This work proposes AspMem, a generative method that contains an array of memory cells to store aspect-related knowledge. This explicit memory can help obtain a better opinion representation and infer the aspect information more precisely. We evaluate this method on both aspect identification and opinion summarization tasks. Our experiments show that AspMem outperforms the state-of-the-art methods even though, unlike the baselines, it does not rely on human supervision which is carefully handcrafted for the given tasks.