| Challenge: | Recent advances in text autoencoders have significantly improved the quality of the latent space, allowing models to generate consistent text from aggregated latent vectors. |
| Approach: | They develop a framework which searches input-output word overlap for latent vector aggregation. |
| Outcome: | The proposed framework improves the quality of the latent space and establishes state-of-the-art performance on two opinion summarization benchmarks. |
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Unsupervised Opinion Summarisation in the Wasserstein Space (2022.emnlp-main)
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| Challenge: | Recent work on opinion summarisation of social media posts has focused on reviews . however, it is important to capture user opinions in online discussions over specific topics . |
| Approach: | They propose an unsupervised opinion summarisation model which uses the Wasserstein distance to generate a single summary from a group of documents. |
| Outcome: | The proposed model outperforms the state-of-the-art on ROUGE metrics and produces the best summaries with respect to meaning preservation according to human evaluations. |
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. |
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. |
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. |
Improving Latent Alignment in Text Summarization by Generalizing the Pointer Generator (D19-1)
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| Challenge: | Modern pointer generators only capture exact word matches, ignoring possible inflections or abstractions, which restricts its power of capturing richer latent alignment. |
| Approach: | They propose a pointer generator architecture that allows the model to "edit" pointed tokens instead of always copying them. |
| Outcome: | The proposed model captures more latent alignment relations than exact word matches and generates higher-quality summaries validated by both qualitative and quantitative evaluations. |
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 Abstractive Opinion Summarization by Generating Sentences with Tree-Structured Topic Guidance (2021.tacl-1)
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| Challenge: | Abstractive summarization is a novel method for opinionated texts . it uses a recursive Gaussian mixture to generate topic sentences . |
| Approach: | They propose an unsupervised abstractive summarization method for opinionated texts . they alternate the unimodal Gaussian prior with a recursive Gausssian mixture . |
| Outcome: | The proposed method generates topic sentences with tree-structured topic guidance, which are more informative and cover more input contents than the current model. |
Frustratingly Easy Model Ensemble for Abstractive Summarization (D18-1)
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| Challenge: | Existing studies on compressing or distilling ensemble models have shown that they increase computational costs and reduce performance. |
| Approach: | They propose an unsupervised method that combines multiple models by selecting a majority-like output in post-processing. |
| Outcome: | The proposed method performs better than the current ensemble methods on a news-headline-generation task. |
Disentangling Text Representation With Counter-Template For Unsupervised Opinion Summarization (2023.findings-acl)
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| Challenge: | Existing approaches for unsupervised opinion summarization are based on reconstruction model, but selection is too coarse as not all information in each input is equally essential for the summary. |
| Approach: | They propose a framework for unsupervised opinion summarization based on text representation disentanglement with counter-template. |
| Outcome: | The proposed framework outperforms the state-of-the-art models on quality and stability on two benchmark datasets. |
SummVD : An efficient approach for unsupervised topic-based text summarization (2022.aacl-main)
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| Challenge: | SummVD is an unsupervised extractive summarization method that uses word clustering to reduce word embeddings. |
| Approach: | They propose a method for automatic unsupervised extractive summarization using word clustering and singular value decomposition. |
| Outcome: | The proposed method outperforms other extractive methods using several corpora of different nature. |