| Challenge: | Existing methods for abstractive summarization are limited and cannot be easily sourced. |
| Approach: | They propose a supervised learning model which learns to denoise the input and generate original reviews. |
| Outcome: | The proposed model improves on the baselines of abstractive and extractive models on a large dataset with only a few reviews and no ground truth summaries. |
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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. |
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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. |
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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. |
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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. |
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| 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. |
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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. |
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Tejpalsingh Siledar, Suman Banerjee, Amey Patil, Sudhanshu Singh, Muthusamy Chelliah, Nikesh Garera, Pushpak Bhattacharyya
| Challenge: | Existing approaches to generate general and aspect-specific opinion summarization are limited due to their reliance on human-specified aspects and seed words. |
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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. |
TED: A Pretrained Unsupervised Summarization Model with Theme Modeling and Denoising (2020.findings-emnlp)
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| Challenge: | Existing abstractive summarization models ignore abundant unlabeled corpora resources . TED outperforms all unsupervised abstractive baselines on NYT, CNN/DM and English Gigaword datasets . |
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Product Description and QA Assisted Self-Supervised Opinion Summarization (2024.findings-naacl)
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Tejpalsingh Siledar, Rupasai Rangaraju, Sankara Muddu, Suman Banerjee, Amey Patil, Sudhanshu Singh, Muthusamy Chelliah, Nikesh Garera, Swaprava Nath, Pushpak Bhattacharyya
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