| Challenge: | Existing work on product reviews summarization focuses on generating concise, coherent and informative summaries, but this task is challenging. |
| Approach: | They propose a product reviews summarization task that employs a large pre-trained Transformer-based model and a method for ranking these summaries according to desired criteria. |
| Outcome: | The proposed system avoids the problem of self-contradiction by ranking the summaries according to desired criteria. |
Similar Papers
Aspect-based summarization of pros and cons in unstructured product reviews (C18-1)
Copied to clipboard
| Challenge: | SynPat, a system based on syntactic phrases selected on the basis of valence scores, and a neural-network-based system trained on clusters of word-embedding encodings of similar pros and cons are compared to SynPat. |
| Approach: | They propose to use syntactic phrases selected on the basis of valence scores to generate pros and cons summaries. |
| Outcome: | The proposed systems outperform the baseline systems on held-out reviews with gold-standard pros and cons and on human annotators on relevance and completeness. |
A Hybrid Approach to Cross-lingual Product Review Summarization (2022.emnlp-industry)
Copied to clipboard
| Challenge: | Existing methods for summarizing product reviews with thousands of reviews are inefficient and time consuming. |
| Approach: | They propose an unsupervised extractive step and a supervised abstractive step to generate a short summary in any language. |
| Outcome: | The proposed model is as good as human written summaries in coherence, informativeness, non-redundancy, and fluency as human summary summators. |
Few-Shot Learning for Opinion Summarization (2020.emnlp-main)
Copied to clipboard
| Challenge: | a recent study shows that abstractive summarization models fail to capture their essential properties due to the high cost of summary production. |
| Approach: | They propose a few-shot framework for abstractive opinion summarization that bootstraps the output of an unsupervised model. |
| Outcome: | The proposed framework outperforms extractive and abstractive methods on Amazon and Yelp datasets. |
Multi-Objective Forward Reasoning and Multi-Reward Backward Refinement for Product Review Summarization (2024.lrec-main)
Copied to clipboard
| Challenge: | Product review summarization aims to generate a concise summary based on product reviews . factual accuracy, aspect comprehensiveness, and content relevance are challenges . |
| Approach: | They propose an FB-Thinker framework to improve product review summarization ability . they propose two Chinese product review summary datasets for instruction-tuning and evaluation . |
| Outcome: | The proposed framework improves product review summarization with forward reasoning and backward refinement. |
Unsupervised Opinion Summarization as Copycat-Review Generation (2020.acl-main)
Copied to clipboard
| Challenge: | Recent work on opinion summarization has focused on extracting fragments from reviews, but we use novel sentences to generate abstractive summaries. |
| Approach: | They propose an abstractive summarizer which does not use summaries in training and is trained end-to-end on a large collection of reviews. |
| Outcome: | The proposed model produces fluent and coherent summaries reflecting consensus opinions on Amazon and Yelp reviews. |
Learning Opinion Summarizers by Selecting Informative Reviews (2021.emnlp-main)
Copied to clipboard
| Challenge: | supervised summarization has been traditionally approached with unsupervised, weakly-supervised and few-shot learning techniques. |
| Approach: | They propose to combine a large dataset of opinion summaries with user reviews to form a supervised summarizer. |
| Outcome: | The proposed method improves the quality of summarization and reduces hallucinations in the summarizer. |
RevieWeaver: Weaving Together Review Insights by Leveraging LLMs and Semantic Similarity (2025.naacl-industry)
Copied to clipboard
| Challenge: | RevieWeaver extracts key product features and provides concise review summaries . a condensed list of key features, pros, and cons, along with a brief summary of customer opinions can help mitigate this issue. |
| Approach: | They propose a framework that extracts key product features and provides concise review summaries. |
| Outcome: | The proposed framework scales efficiently to 30 million reviews and ensures reproducibility and controllability. |
End-to-End Aspect-Guided Review Summarization at Scale (2025.emnlp-industry)
Copied to clipboard
Ilya Boytsov, Vinny DeGenova, Mikhail Balyasin, Joseph Walt, Caitlin Eusden, Marie-Claire Rochat, Margaret Pierson
| Challenge: | Existing methods to generate concise product review summaries are prone to hallucination, omission of important facts, and factual errors. |
| Approach: | They propose a large language model-based system that combines aspect-based sentiment analysis with guided summarization to generate concise product review summaries. |
| Outcome: | The proposed system generates concise and interpretable product review summaries using a large language model (LLM) dataset. |
Efficient Few-Shot Fine-Tuning for Opinion Summarization (2022.findings-naacl)
Copied to clipboard
| Challenge: | Abstractive summarization models are typically pre-trained on large amounts of generic texts . large annotated datasets of reviews paired with reference summaries are not available . |
| Approach: | They propose a few-shot method which uses adapters to store in-domain knowledge . they pre-train adapters on unannotated customer reviews and fine-tune them on annotated datasets . |
| Outcome: | The proposed method can store in-domain knowledge and improves on large annotated reviews . it improves coherence and redundancies on the Amazon and Yelp datasets . |
Not All Reviews Are Equal: Towards Addressing Reviewer Biases for Opinion Summarization (P19-2)
Copied to clipboard
| Challenge: | Existing research focuses on mining for opinions from review texts and ignores reviewers. |
| Approach: | They propose to model reviewer biases from review texts and learn a bias-aware opinion representation. |
| Outcome: | The proposed method includes balanced opinions from reviewers with different biases and preferences. |