mlsanityData to 7 Oct 2026

Beyond Opinion Mining: Summarizing Opinions of Customer Reviews

arXiv 2206.01543 ↗First posted 3 Jun 2022cs.CL cs.AI cs.LGcited by 9

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Abstract (arXiv)

Customer reviews are vital for making purchasing decisions in the Information Age. Such reviews can be automatically summarized to provide the user with an overview of opinions. In this tutorial, we present various aspects of opinion summarization that are useful for researchers and practitioners. First, we will introduce the task and major challenges. Then, we will present existing opinion summarization solutions, both pre-neural and neural. We will discuss how summarizers can be trained in the unsupervised, few-shot, and supervised regimes. Each regime has roots in different machine learning methods, such as auto-encoding, controllable text generation, and variational inference. Finally, we will discuss resources and evaluation methods and conclude with the future directions. This three-hour tutorial will provide a comprehensive overview over major advances in opinion summarization. The listeners will be well-equipped with the knowledge that is both useful for research and practical applications.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 26 of its 51 references that are in the map. First posted on arXiv 3 Jun 2022.

Builds on

26 of 51 references.

References · 26

  1. 2013Auto-Encoding Variational Bayescited by 3,980
  2. 2020Longformer: The Long-Document Transformercited by 3,362
  3. 2017Get To The Point: Summarization with Pointer-Generator Networkscited by 1,281
  4. 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,100
  5. 2020On Faithfulness and Factuality in Abstractive Summarizationcited by 895
  6. 2015Generating Sentences from a Continuous Spacecited by 607
  7. 2015A Neural Attention Model for Abstractive Sentence Summarizationcited by 394
  8. 2020Extractive Opinion Summarization in Quantized Transformer Spacescited by 76
  9. 2018Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervisedcited by 75
  10. 2019Unsupervised Opinion Summarization as Copycat-Review Generationcited by 68
  11. 2021Aspect-Controllable Opinion Summarizationcited by 47
  12. 2016Neural Network-Based Abstract Generation for Opinions and Argumentscited by 46
14 more
  1. 2020Unsupervised Opinion Summarization with Noising and Denoisingcited by 37
  2. 2020OpinionDigest: A Simple Framework for Opinion Summarizationcited by 37
  3. 2019Informative and Controllable Opinion Summarizationcited by 27
  4. 2020Unsupervised Opinion Summarization with Content Planningcited by 27
  5. 2021Learning Opinion Summarizers by Selecting Informative Reviewscited by 25
  6. 2020Self-Supervised and Controlled Multi-Document Opinion Summarizationcited by 23
  7. 2021Convex Aggregation for Opinion Summarizationcited by 21
  8. 2021Self-Supervised Multimodal Opinion Summarizationcited by 18
  9. 2022Efficient Few-Shot Fine-Tuning for Opinion Summarizationcited by 16
  10. 2021Unsupervised Abstractive Opinion Summarization by Generating Sentences with Tree-Structured Topic Guidancecited by 14
  11. 2019Weakly-Supervised Opinion Summarization by Leveraging External Informationcited by 11
  12. 2020Read what you need: Controllable Aspect-based Opinion Summarization of Tourist Reviewscited by 9
  13. 2016Query-Focused Opinion Summarization for User-Generated Contentcited by 7
  14. 2020Massive Multi-Document Summarization of Product Reviews with Weak Supervisioncited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 3; 2025: 0; 2026: 2.

9 papers.