Fine-Grained Opinion Summarization with Minimal Supervision

TL;DR

FineSum is a novel framework for opinion summarization that requires minimal supervision, performs fine-grained analysis at the sub-aspect level, and summarizes opinions using coherent phrases, validated by automatic and human evaluations.

Contribution

It introduces a minimal supervision approach for fine-grained opinion analysis and phrase-based summarization in opinion summarization tasks.

Findings

Effective automatic identification of opinion phrases.

Successful classification into aspects and sentiments.

Validated by benchmark and human evaluation.

Abstract

Opinion summarization aims to profile a target by extracting opinions from multiple documents. Most existing work approaches the task in a semi-supervised manner due to the difficulty of obtaining high-quality annotation from thousands of documents. Among them, some use aspect and sentiment analysis as a proxy for identifying opinions. In this work, we propose a new framework, FineSum, which advances this frontier in three aspects: (1) minimal supervision, where only aspect names and a few aspect/sentiment keywords are available; (2) fine-grained opinion analysis, where sentiment analysis drills down to the sub-aspect level; and (3) phrase-based summarization, where opinion is summarized in the form of phrases. FineSum automatically identifies opinion phrases from the raw corpus, classifies them into different aspects and sentiments, and constructs multiple fine-grained opinion clusters under each aspect/sentiment. Each cluster consists of semantically coherent phrases, expressing uniform opinions towards certain sub-aspect or characteristics (e.g., positive feelings for ``burgers'' in the ``food'' aspect). An opinion-oriented spherical word embedding space is trained to provide weak supervision for the phrase classifier, and phrase clustering is performed using the aspect-aware contextualized embedding generated from the phrase classifier. Both automatic evaluation on the benchmark and quantitative human evaluation validate the effectiveness of our approach.