mlsanityData to 7 Oct 2026

Examining Bias in Opinion Summarisation Through the Perspective of Opinion Diversity

arXiv 2306.04424 ↗First posted 7 Jun 2023cs.CLcited by 14

Not on the map. Why?

Abstract (arXiv)

Opinion summarisation is a task that aims to condense the information presented in the source documents while retaining the core message and opinions. A summary that only represents the majority opinions will leave the minority opinions unrepresented in the summary. In this paper, we use the stance towards a certain target as an opinion. We study bias in opinion summarisation from the perspective of opinion diversity, which measures whether the model generated summary can cover a diverse set of opinions. In addition, we examine opinion similarity, a measure of how closely related two opinions are in terms of their stance on a given topic, and its relationship with opinion diversity. Through the lens of stances towards a topic, we examine opinion diversity and similarity using three debatable topics under COVID-19. Experimental results on these topics revealed that a higher degree of similarity of opinions did not indicate good diversity or fairly cover the various opinions originally presented in the source documents. We found that BART and ChatGPT can better capture diverse opinions presented in the source documents.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 22 of its 40 references that are in the map. First posted on arXiv 7 Jun 2023.

Builds on

22 of 40 references.

References · 22

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  3. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  4. 2017Deep reinforcement learning from human preferencescited by 3,846
  5. 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,100
  6. 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 951
  7. 2016Demographic Dialectal Variation in Social Media: A Case Study of African-American Englishcited by 194
  8. 2019Fine-tune BERT for Extractive Summarizationcited by 122
  9. 2023Exploring the Limits of ChatGPT for Query or Aspect-based Text Summarizationcited by 122
  10. 2020COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twittercited by 96
  11. 2018Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervisedcited by 75
  12. 2020Multi-View Sequence-to-Sequence Models with Conversational Structure for Abstractive Dialogue Summarizationcited by 72
10 more
  1. 2019Unsupervised Opinion Summarization as Copycat-Review Generationcited by 68
  2. 2020From Arguments to Key Points: Towards Automatic Argument Summarizationcited by 60
  3. 2019Classification and Clustering of Arguments with Contextualized Word Embeddingscited by 57
  4. 2021ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Miningcited by 37
  5. 2020Quantitative Argument Summarization and Beyond: Cross-Domain Key Point Analysiscited by 31
  6. 2017Measuring the Similarity of Sentential Arguments in Dialogcited by 23
  7. 2021Every Bite Is an Experience: Key Point Analysis of Business Reviewscited by 19
  8. 2020Dialect Diversity in Text Summarization on Twittercited by 17
  9. 2022Template-based Abstractive Microblog Opinion Summarisationcited by 9
  10. 2021Fairness for Whom? Understanding the Reader's Perception of Fairness in Text Summarizationcited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 5; 2025: 4; 2026: 3.

14 papers.

Sort:

  1. 2024Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMscited by 224
  2. 2023Bias in News Summarization: Measures, Pitfalls and Corporacited by 18
  3. 2023Fair Abstractive Summarization of Diverse Perspectivescited by 17
  4. 2024Bias in Opinion Summarisation from Pre-training to Adaptation: A Case Study in Political Biascited by 12
  5. 2024An Empirical Analysis of Diversity in Argument Summarizationcited by 9
  6. 2025LLMs Do Not See Age: Assessing Demographic Bias in Automated Systematic Review Synthesiscited by 5
  7. 2025A survey of diversity quantification in natural language processing: The why, what, where and howcited by 4
  8. 2024Summarization of Opinionated Political Documents with Varied Perspectivescited by 3
  9. 2025Less Is More? Examining Fairness in Pruned Large Language Models for Summarising Opinionscited by 2
  10. 2025REFER: Mitigating Bias in Opinion Summarisation via Frequency Framed Promptingcited by 2
  11. 2026Faithful Summarisation under Disagreement via Belief-Level Aggregationcited by 1
  12. 2026When Bigger Isn't Better: A Comprehensive Fairness Evaluation of Political Bias in Multi-News Summarisationcited by 1
2 more
  1. 2026Structuring the Space of Perspectivescited by 1
  2. 2024Fair Summarization: Bridging Quality and Diversity in Extractive Summariescited by 0