Review Generation
21 papers with code • 0 benchmarks • 3 datasets
Benchmarks
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Most implemented papers
DP-GAN: Diversity-Promoting Generative Adversarial Network for Generating Informative and Diversified Text
Existing text generation methods tend to produce repeated and "boring" expressions.
Unsupervised Opinion Summarization as Copycat-Review Generation
At test time, when generating summaries, we force the novelty to be minimal, and produce a text reflecting consensus opinions.
Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)
For a long time, different recommendation tasks typically require designing task-specific architectures and training objectives.
Personalized Review Generation By Expanding Phrases and Attending on Aspect-Aware Representations
In this paper, we focus on the problem of building assistive systems that can help users to write reviews.
Diversity-Promoting GAN: A Cross-Entropy Based Generative Adversarial Network for Diversified Text Generation
Existing text generation methods tend to produce repeated and {''}boring{''} expressions.
Judge the Judges: A Large-Scale Evaluation Study of Neural Language Models for Online Review Generation
We conduct a large-scale, systematic study to evaluate the existing evaluation methods for natural language generation in the context of generating online product reviews.
Generating Long and Informative Reviews with Aspect-Aware Coarse-to-Fine Decoding
In this paper, we propose a novel review generation model by characterizing an elaborately designed aspect-aware coarse-to-fine generation process.
Knowledge-Enhanced Personalized Review Generation with Capsule Graph Neural Network
First, based on graph capsules, we adaptively learn aspect capsules for inferring the aspect sequence.
ReviewRobot: Explainable Paper Review Generation based on Knowledge Synthesis
To assist human review process, we build a novel ReviewRobot to automatically assign a review score and write comments for multiple categories such as novelty and meaningful comparison.
Multimodal Review Generation with Privacy and Fairness Awareness
Users express their opinions towards entities (e. g., restaurants) via online reviews which can be in diverse forms such as text, ratings, and images.