mlsanityData to 14 Oct 2026

Comparative Snippet Generation

arXiv 2206.05473 ↗First posted 11 Jun 2022cs.CLcited by 0

Not on the map. Why?

Abstract (arXiv)

We model product reviews to generate comparative responses consisting of positive and negative experiences regarding the product. Specifically, we generate a single-sentence, comparative response from a given positive and a negative opinion. We contribute the first dataset for this task of Comparative Snippet Generation from contrasting opinions regarding a product, and a performance analysis of a pre-trained BERT model to generate such snippets.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 21 references that are in the map. First posted on arXiv 11 Jun 2022.

Builds on

13 of 21 references.

References · 13

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  4. 2017Neural Discrete Representation Learningcited by 4,712
  5. 2014Convolutional Neural Networks for Sentence Classificationcited by 1,127
  6. 2019Leveraging Pre-trained Checkpoints for Sequence Generation Taskscited by 182
  7. 2020Extractive Opinion Summarization in Quantized Transformer Spacescited by 76
  8. 2019AmazonQA: A Review-Based Question Answering Taskcited by 43
  9. 2020OpinionDigest: A Simple Framework for Opinion Summarizationcited by 37
  10. 2019DiscoFuse: A Large-Scale Dataset for Discourse-Based Sentence Fusioncited by 29
  11. 2019Informative and Controllable Opinion Summarizationcited by 27
  12. 2017Multiple Instance Learning Networks for Fine-Grained Sentiment Analysiscited by 23
1 more
  1. 2020Semantically Driven Sentence Fusion: Modeling and Evaluationcited by 4

Built on by

Not cited yet.