| Challenge: | Existing methods for analyzing and summarizing customer reviews are based on a number of prominent review aspects. |
| Approach: | They propose a framework for extracting the most prominent aspects of a given product type from textual reviews. |
| Outcome: | The proposed framework extracts K most prominent aspect terms which do not overlap semantically without supervision. |
Similar Papers
Using Aspect Extraction Approaches to Generate Review Summaries and User Profiles (N18-3)
Copied to clipboard
| Challenge: | Existing work on aspect extraction from reviews has focused on capturing aspects of user preferences. |
| Approach: | They propose a neural model for aspect extraction from reviews . they use a k-means baseline to extract canonical sentences of various aspects from reviews. |
| Outcome: | The proposed model performs well on two tasks. |
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. |
InsightNet : Structured Insight Mining from Customer Feedback (2023.emnlp-industry)
Copied to clipboard
Sandeep Sricharan Mukku, Manan Soni, Chetan Aggarwal, Jitenkumar Rana, Promod Yenigalla, Rashmi Patange, Shyam Mohan
| Challenge: | Existing methods for extracting structured insights from reviews suffer from drawbacks . lack of structure, non-standard aspect names, lack of abundant training data limit their effectiveness and applicability. |
| Approach: | They propose a semi-supervised multi-level taxonomy from raw customer reviews and a semantic similarity heuristic approach to generate labelled data. |
| Outcome: | The proposed approach outperforms existing methods in structure, hierarchy and completeness. |
Aspect-Category-Opinion-Sentiment Quadruple Extraction with Implicit Aspects and Opinions (2021.acl-long)
Copied to clipboard
| Challenge: | Existing studies in aspect-based sentiment analysis ignore aspects and opinions in product reviews. |
| Approach: | They propose a task to extract aspect-category-opinion-sentiment quadruples from review sentences . they construct two new datasets that contain annotations of implicit aspects and opinions . |
| Outcome: | The proposed task provides full support for aspect-based sentiment analysis with implicit aspects and opinions. |
Aspect-aware Unsupervised Extractive Opinion Summarization (2023.findings-acl)
Copied to clipboard
| Challenge: | Extractive opinion summarization extracts sentences from reviews to represent the prevalent opinions about a product or service. |
| Approach: | They propose a method for unsupervised extractive opinion summarization that automatically identifies the aspects described in review sentences and extracts sentences based on their aspects. |
| Outcome: | The proposed method improves aspect coverage and performs well on multiple opinion summarization datasets. |
WikiAsp: A Dataset for Multi-domain Aspect-based Summarization (2021.tacl-1)
Copied to clipboard
| Challenge: | Existing aspects-based summarization models are domain-specific due to large differences in the type of aspects for different domains. |
| Approach: | They propose a large-scale dataset for multi-domain aspect-based summarization using Wikipedia articles from 20 different domains. |
| Outcome: | The proposed model is based on Wikipedia articles from 20 different domains and uses the section titles and boundaries of each article as a proxy for aspect annotation. |
Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction (P18-2)
Copied to clipboard
| Challenge: | Recent supervised deep learning models have achieved state-of-the-art performance, but there are two other considerations that are important. |
| Approach: | They propose a supervised aspect extraction model using general-purpose embeddings and domain-specific embeddables. |
| Outcome: | The proposed model outperforms state-of-the-art methods without supervision and achieves very good results. |
MARS: Multilingual Aspect-centric Review Summarisation (2024.emnlp-industry)
Copied to clipboard
| Challenge: | Existing methods for summarizing customer feedback are not able to extract actionable reviews into a specific target language. |
| Approach: | They propose a framework involving extract-then-summarise to summariser customer feedback into a specific language. |
| Outcome: | The proposed framework improves abstractive baselines and efficiency to real-time systems. |
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. |
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. |