Large-Scale and Multi-Perspective Opinion Summarization with Diverse Review Subsets (2023.findings-emnlp)
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| Challenge: | Existing methods for opinion summarization are deficient in epitomizing extensive reviews and offering opinion summaries from various angles. |
| Approach: | They propose a supervised opinion summarization framework that takes sentiment orientation into account and trains the summarizer to learn from sub-optimal and optimal review subsets. |
| Outcome: | The proposed framework generates pros, cons, and verdict summaries from hundreds of input reviews. |
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| Challenge: | Existing studies focus on unsupervised opinion summarization and treat it as a normal multi-document summarizing task. |
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| Challenge: | Existing methods for opinion summarization use text data, but non-text data are less abundant. |
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| Challenge: | supervised summarization has been traditionally approached with unsupervised, weakly-supervised and few-shot learning techniques. |
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| Challenge: | Existing opinion summarization methods are insufficient to help users compare multiple choices. |
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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: | 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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Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised (D18-1)
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| Challenge: | Existing methods for opinion summarization are knowledge-lean and require light supervision. |
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Ilya Boytsov, Vinny DeGenova, Mikhail Balyasin, Joseph Walt, Caitlin Eusden, Marie-Claire Rochat, Margaret Pierson
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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. |
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Towards Opinion Summarization of Customer Reviews (P18-3)
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| Challenge: | Existing methods to summarize text are limited to small, homogeneous datasets . authors outline future directions to solve these problems . |
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