Identifying Helpful Sentences in Product Reviews

TL;DR

This paper introduces a novel task of extracting a single, helpful, sentiment-aware sentence from product reviews to aid quick decision-making, especially in voice shopping, supported by a new dataset and a competitive extraction model.

Contribution

It proposes a new task of identifying a single representative helpful sentence from reviews, along with creating a crowd-sourced dataset and a model that outperforms baselines.

Findings

The dataset is reliable despite subjectivity.

The proposed model effectively extracts sentiment-aware helpful sentences.

Outperforms baseline methods in extraction accuracy.

Abstract

In recent years online shopping has gained momentum and became an important venue for customers wishing to save time and simplify their shopping process. A key advantage of shopping online is the ability to read what other customers are saying about products of interest. In this work, we aim to maintain this advantage in situations where extreme brevity is needed, for example, when shopping by voice. We suggest a novel task of extracting a single representative helpful sentence from a set of reviews for a given product. The selected sentence should meet two conditions: first, it should be helpful for a purchase decision and second, the opinion it expresses should be supported by multiple reviewers. This task is closely related to the task of Multi Document Summarization in the product reviews domain but differs in its objective and its level of conciseness. We collect a dataset in English of sentence helpfulness scores via crowd-sourcing and demonstrate its reliability despite the inherent subjectivity involved. Next, we describe a complete model that extracts representative helpful sentences with positive and negative sentiment towards the product and demonstrate that it outperforms several baselines.