| Challenge: | Existing methods for opinion summarization are expensive and do not deal with contradictory statements. |
| Approach: | They propose an unsupervised abstractive summarization neural system that generates short summaries of reviews in a vector space. |
| Outcome: | The proposed system can generate short summaries of user-generated reviews in a short paragraph, while nobody reads all reviews. |
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Attributable and Scalable Opinion Summarization (2023.acl-long)
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| Challenge: | Existing methods for opinion summarization encode sentences from customer reviews into a hierarchical discrete latent space. |
| Approach: | They propose a method that encodes customer reviews into a hierarchical discrete latent space and then identifies common opinions based on their frequency. |
| Outcome: | The proposed method generates summaries that are more informative than previous work and more grounded in the input reviews. |
Self-Supervised and Controlled Multi-Document Opinion Summarization (2021.eacl-main)
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| Challenge: | Existing unsupervised methods for summarizing reviews are based on bootstrapping and require a combination of loss functions or hierarchical latent variables to ensure that the generated summaries remain on-topic. |
| Approach: | They propose a self-supervised setup that considers an individual document as a target summary for a set of similar documents. |
| Outcome: | The proposed setup makes training simpler than previous approaches by relying only on standard log-likelihood loss and mainstream models. |
Unsupervised Opinion Summarization as Copycat-Review Generation (2020.acl-main)
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| 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. |
Few-Shot Learning for Opinion Summarization (2020.emnlp-main)
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| 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. |
Unsupervised Neural Single-Document Summarization of Reviews via Learning Latent Discourse Structure and its Ranking (P19-1)
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| Challenge: | Currently, unsupervised summarization is widely used for product reviews on E-commerce websites. |
| Approach: | They propose an unsupervised model that learns the latent discourse tree without an external parser and generates a concise summary. |
| Outcome: | The proposed model outperforms other unsupervised approaches for relatively long reviews and is competitive with or better than supervised models. |
Aspect-based summarization of pros and cons in unstructured product reviews (C18-1)
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| 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. |
Aspect and Sentiment Aware Abstractive Review Summarization (C18-1)
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| Challenge: | Abstractive summarization is a task that generates short and concise summaries of user generated reviews. |
| Approach: | They propose an interactive attention mechanism to learn the representations of context and aspect words within reviews, acted as an encoder. |
| Outcome: | The proposed model achieves impressive results compared to other strong competitors on a real-life dataset. |
Aspect-Controllable Opinion Summarization (2021.emnlp-main)
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| Challenge: | Recent work on opinion summarization produces general summaries based on reviews and popularity of opinions expressed in them. |
| Approach: | They propose an approach that generates customized opinion summaries based on aspect queries. |
| Outcome: | The proposed model outperforms the current state of the art and generates personalized summaries by controlling the number of aspects discussed in them. |
Informative and Controllable Opinion Summarization (2021.eacl-main)
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| Challenge: | Existing methods for opinion summarization use a two-stage extractive and abstractive approach to generate summaries for reviews of a specific target. |
| Approach: | They propose a framework for opinion summarization that condenses all input reviews into multiple dense vectors which serve as input to an abstractive model. |
| Outcome: | The proposed framework produces more informative summaries and allows to take user preferences into account using a zero-shot customization technique. |
Unsupervised Extractive Opinion Summarization Using Sparse Coding (2022.acl-long)
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| Challenge: | Existing methods for opinion summarization rely on human annotations, which may not be feasible. |
| Approach: | They propose to perform opinion summarization in an unsupervised manner by using a dictionary learning algorithm that implicitly captures semantic information from the review text. |
| Outcome: | The proposed algorithm performs well on SPACE and AMAZON datasets and performs controllable summarization to generate aspect-specific summaries using only a few samples. |