# Identifying Helpful Sentences in Product Reviews

arXiv: https://arxiv.org/abs/2104.09792 · HTML: https://mlsanity.com/paper/2104.09792/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 40 references that are in the map.
- First posted on arXiv 20 Apr 2021.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://mlsanity.com/paper/1808.08745/) (2018)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [Advances in Pre-Training Distributed Word Representations](https://mlsanity.com/paper/1712.09405/) (2017)
- [Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised](https://mlsanity.com/paper/1808.08858/) (2018)
- [Unsupervised Opinion Summarization as Copycat-Review Generation](https://mlsanity.com/paper/1911.02247/) (2019)
- [Unsupervised Neural Single-Document Summarization of Reviews via Learning Latent Discourse Structure and its Ranking](https://mlsanity.com/paper/1906.05691/) (2019)
- [Massive Multi-Document Summarization of Product Reviews with Weak Supervision](https://mlsanity.com/paper/2007.11348/) (2020)
- [A Hierarchical End-to-End Model for Jointly Improving Text Summarization and Sentiment Classification](https://mlsanity.com/paper/1805.01089/) (2018)
- [Few-Shot Learning for Opinion Summarization](https://mlsanity.com/paper/2004.14884/) (2020)

## Built on by (most cited first, 9 of 9)

- [SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning](https://mlsanity.com/paper/2209.05040/) (2022)
- [Decision-Focused Summarization](https://mlsanity.com/paper/2109.06896/) (2021)
- [On the Role of Reviewer Expertise in Temporal Review Helpfulness Prediction](https://mlsanity.com/paper/2303.00923/) (2023)
- [OpinioRAG: Towards Generating User-Centric Opinion Highlights from Large-scale Online Reviews](https://mlsanity.com/paper/2509.00285/) (2025)
- [Decision-Making with Deliberation: Meta-reviewing as a Document-grounded Dialogue](https://mlsanity.com/paper/2508.05283/) (2025)
- [Towards Bridging Review Sparsity in Recommendation with Textual Edge Graph Representation](https://mlsanity.com/paper/2508.01128/) (2025)
- [Generating Tips from Song Reviews: A New Dataset and Framework](https://mlsanity.com/paper/2205.06985/) (2022)
- [AaKOS: Aspect-adaptive Knowledge-based Opinion Summarization](https://mlsanity.com/paper/2306.05537/) (2023)
- [Information Types in Product Reviews](https://mlsanity.com/paper/2502.14335/) (2025)

## Abstract (arXiv)

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.