natural-language-processing

Review Generation

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in review-generation

Trend

Dataset

Best Model

Actions

No benchmarks available.

Libraries

i

Use these libraries to find review-generation models and implementations

Datasets

MOPRD

ReviewRobot Dataset

ICLR Database

Subtasks

No subtasks available.

Most implemented papers

DP-GAN: Diversity-Promoting Generative Adversarial Network for Generating Informative and Diversified Text

Xu Sun, Jingjing Xu, Junyang Lin, Bingzhen Wei, Xuancheng Ren, Wei Li•Sun Feb 04 2018

A novel language-model based discriminator is proposed, which can better distinguish novel text from repeated text without the saturation problem compared with existing classifier-based discriminators.

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Unsupervised Opinion Summarization as Copycat-Review Generation

Mirella Lapata, Arthur Brazinskas, Ivan Titov•Tue Nov 05 2019

A generative model for a review collection is defined which capitalizes on the intuition that when generating a new review given a set of other reviews of a product, the authors should be able to control the “amount of novelty” going into the new review or, equivalently, vary the extent to which it deviates from the input.

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Personalized Review Generation By Expanding Phrases and Attending on Aspect-Aware Representations

Julian McAuley, Jianmo Ni•Sat Jun 30 2018

Experimental results show that the model successfully learns representations capable of generating coherent and diverse reviews and discover those aspects that users are more inclined to discuss and bias the generated text toward their personalized aspect preferences.

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Diversity-Promoting GAN: A Cross-Entropy Based Generative Adversarial Network for Diversified Text Generation

Xu Sun, Jingjing Xu, Junyang Lin, Xuancheng Ren•Sun Feb 04 2018

A novel language-model based discriminator is proposed, which can better distinguish novel text from repeated text without the saturation problem compared with existing classifier-based discriminators.

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Judge the Judges: A Large-Scale Evaluation Study of Neural Language Models for Online Review Generation

Cristina Garbacea, Samuel Carton, Shiyan Yan, Qiaozhu Mei•Tue Jan 01 2019

A large-scale, systematic study to evaluate the existing evaluation methods for natural language generation in the context of generating online product reviews finds lexical diversity an intriguing metric that is indicative of the assessments of different evaluators.

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ReviewRobot: Explainable Paper Review Generation based on Knowledge Synthesis

Nazneen Rajani, Qingyun Wang, Lifu Huang, Heng Ji, Kevin Knight, Qi Zeng•Mon Oct 12 2020

A novel ReviewRobot is built to automatically assign a review score and write comments for multiple categories such as novelty and meaningful comparison, and can serve as an assistant for paper reviewers, program chairs and authors.

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Multimodal Review Generation with Privacy and Fairness Awareness

Duc-Trong Le, Xuan-Son Vu, Thanh-Son Nguyen, Lili Jiang•Mon Nov 30 2020

MG-PriFair is the first to bring user privacy and sentiment fairness into the review generation task and is capable of generating plausibly long reviews while controlling the amount of exploited user data and using the least sentiment biased word embeddings.

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Generating Long and Informative Reviews with Aspect-Aware Coarse-to-Fine Decoding

Yang Song, Wayne Xin Zhao, Junyi Li, Ji-Rong Wen•Mon Jun 10 2019

A novel review generation model is proposed by characterizing an elaborately designed aspect-aware coarse-to-fine generation process that is able to jointly utilize aspect semantics, syntactic sketch, and context information.

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Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)

Zuohui Fu, Shijie Geng, Shuchang Liu, Yingqiang Ge, Yongfeng Zhang•Wed Mar 23 2022

A flexible and unified text-to-text paradigm called “Pretrain, Personalized Prompt, and Predict Paradigm” (P5) for recommendation, which unifies various recommendation tasks in a shared framework and will revolutionize the technical form of recommender systems towards universal recommendation engine.

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Knowledge-Enhanced Personalized Review Generation with Capsule Graph Neural Network

Nicholas Jing Yuan, Wayne Xin Zhao, Ji-rong Wen, Junyi Li, Zhicheng Wei, Siqing Li, Gaole He•Sat Oct 03 2020

A novel knowledgeenhanced PRG model based on capsule graph neural network (CapsGNN) is proposed, which is the first to utilize knowledge graph for the PRG task and is able to enhance user preference at both aspect and word levels.

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