| Challenge: | Existing models that generate user reviews do not consider the hierarchical structure of user reviews, thus their results lack credibility and diversity. |
| Approach: | They propose a model RevGAN that automatically generates controllable user reviews . they use self-attentive recursive autoencoders, conditional discriminators, and personalized decoder . |
| Outcome: | The proposed model outperforms state-of-the-art generation models in terms of sentence quality, coherence, personalization, and human evaluations on real-world datasets. |
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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. |
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Personalized Review Generation By Expanding Phrases and Attending on Aspect-Aware Representations (P18-2)
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| Challenge: | Existing systems that use user and item identity as inputs for review generation are lacking in the field of natural language processing. |
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Retrieval-Augmented Controllable Review Generation (2020.coling-main)
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Automatic Generation of Personalized Comment Based on User Profile (P19-2)
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Pre-trained Personalized Review Summarization with Effective Salience Estimation (2023.findings-acl)
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| Challenge: | Pretrained language models (PLMs) are a new paradigm in text generation for the strong ability of natural language comprehension. |
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Exploiting Rich Textual User-Product Context for Improving Personalized Sentiment Analysis (2023.findings-acl)
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| Challenge: | Typical approaches do not exploit the potential of historical reviews or do not make full use of user/product associations. |
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Improving Document-Level Sentiment Analysis with User and Product Context (2020.coling-main)
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| Challenge: | Existing work that improves document-level sentiment analysis by encoding user and product information has been limited to considering only the text of the current review. |
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Select and Attend: Towards Controllable Content Selection in Text Generation (D19-1)
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A Sentiment Consolidation Framework for Meta-Review Generation (2024.acl-long)
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| Challenge: | Recent advances in abstractive text summarization have created plausible summaries, but it is unclear if they truly possess the capability of information consolidation to generate summary. |
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