OpineSum: Entailment-based self-training for abstractive opinion summarization (2023.findings-acl)
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| Challenge: | Abstractive summarization is promising for fluently comparing opinions from a set of reviews about a place or product. |
| Approach: | They propose a novel method that automatically leverages common opinions across reviews to create powerful abstractive models. |
| Outcome: | The proposed method outperforms strong peer systems in both settings. |
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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. |
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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. |
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Unsupervised Aspect-Based Multi-Document Abstractive Summarization (D19-54)
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| Challenge: | Existing methods for opinion summarization are expensive and do not deal with contradictory statements. |
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Unsupervised Opinion Summarization with Noising and Denoising (2020.acl-main)
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| Challenge: | Existing methods for abstractive summarization are limited and cannot be easily sourced. |
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OpinionDigest: A Simple Framework for Opinion Summarization (2020.acl-main)
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| Challenge: | Abstractive opinion summarization framework outperforms competitors' summarizing frameworks . extractive approaches produce well-formed text, but selecting the most popular opinions is challenging . |
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Learning Opinion Summarizers by Selecting Informative Reviews (2021.emnlp-main)
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| Challenge: | supervised summarization has been traditionally approached with unsupervised, weakly-supervised and few-shot learning techniques. |
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AgreeSum: Agreement-Oriented Multi-Document Summarization (2021.findings-acl)
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| Challenge: | Existing studies on agreement-oriented multidocument summarization have focused on clusters of articles . a recent study focused on the use of a pretraining framework to summarize articles based on the "union" of the articles. |
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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. |
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BRIO: Bringing Order to Abstractive Summarization (2022.acl-long)
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| Challenge: | Abstractive summarization models are often trained with maximum likelihood estimation (MLE) . mLE assumes a deterministic (one-point) target distribution, but can cause performance degradation . |
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Self-Supervised Multimodal Opinion Summarization (2021.acl-long)
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| Challenge: | Existing methods for opinion summarization use text data, but non-text data are less abundant. |
| Approach: | They propose a self-supervised opinion summarization framework that uses non-text data to generate a summary from multiple reviews. |
| Outcome: | The proposed framework is superior to existing methods on Yelp and Amazon datasets. |