Fair Summarization: Bridging Quality and Diversity in Extractive Summaries

• URL: http://arxiv.org/abs/2411.07521v2
• Date: Wed, 13 Nov 2024 04:03:54 GMT
• Title: Fair Summarization: Bridging Quality and Diversity in Extractive Summaries
• Authors: Sina Bagheri Nezhad, Sayan Bandyapadhyay, Ameeta Agrawal,
• Abstract summary: We introduce two novel methods for fair extractive summarization: FairExtract and FairGPT. We evaluate these methods using Divsumm summarization dataset of White-aligned, Hispanic, and African-American dialect tweets.
• Score: 4.214129657411282
• License:
• Abstract: Fairness in multi-document summarization of user-generated content remains a critical challenge in natural language processing (NLP). Existing summarization methods often fail to ensure equitable representation across different social groups, leading to biased outputs. In this paper, we introduce two novel methods for fair extractive summarization: FairExtract, a clustering-based approach, and FairGPT, which leverages GPT-3.5-turbo with fairness constraints. We evaluate these methods using Divsumm summarization dataset of White-aligned, Hispanic, and African-American dialect tweets and compare them against relevant baselines. The results obtained using a comprehensive set of summarization quality metrics such as SUPERT, BLANC, SummaQA, BARTScore, and UniEval, as well as a fairness metric F, demonstrate that FairExtract and FairGPT achieve superior fairness while maintaining competitive summarization quality. Additionally, we introduce composite metrics (e.g., SUPERT+F, BLANC+F) that integrate quality and fairness into a single evaluation framework, offering a more nuanced understanding of the trade-offs between these objectives. This work highlights the importance of fairness in summarization and sets a benchmark for future research in fairness-aware NLP models.

Related papers

• Fair Abstractive Summarization of Diverse Perspectives [103.08300574459783]
  A fair summary should provide a comprehensive coverage of diverse perspectives without underrepresenting certain groups. We first formally define fairness in abstractive summarization as not underrepresenting perspectives of any groups of people. We propose four reference-free automatic metrics by measuring the differences between target and source perspectives.
  arXiv  Detail & Related papers  (2023-11-14T03:38:55Z)
• FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods [84.1077756698332]
  This paper introduces the Fair Fairness Benchmark (textsfFFB), a benchmarking framework for in-processing group fairness methods. We provide a comprehensive analysis of state-of-the-art methods to ensure different notions of group fairness.
  arXiv  Detail & Related papers  (2023-06-15T19:51:28Z)
• DualFair: Fair Representation Learning at Both Group and Individual Levels via Contrastive Self-supervision [73.80009454050858]
  This work presents a self-supervised model, called DualFair, that can debias sensitive attributes like gender and race from learned representations. Our model jointly optimize for two fairness criteria - group fairness and counterfactual fairness.
  arXiv  Detail & Related papers  (2023-03-15T07:13:54Z)
• Learning Informative Representation for Fairness-aware Multivariate Time-series Forecasting: A Group-based Perspective [50.093280002375984]
  Performance unfairness among variables widely exists in multivariate time series (MTS) forecasting models. We propose a novel framework, named FairFor, for fairness-aware MTS forecasting.
  arXiv  Detail & Related papers  (2023-01-27T04:54:12Z)
• Evaluating and Improving Factuality in Multimodal Abstractive Summarization [91.46015013816083]
  We propose CLIPBERTScore to leverage the robustness and strong factuality detection performance between image-summary and document-summary. We show that this simple combination of two metrics in the zero-shot achieves higher correlations than existing factuality metrics for document summarization. Our analysis demonstrates the robustness and high correlation of CLIPBERTScore and its components on four factuality metric-evaluation benchmarks.
  arXiv  Detail & Related papers  (2022-11-04T16:50:40Z)
• MultiFair: Multi-Group Fairness in Machine Learning [52.24956510371455]
  We study multi-group fairness in machine learning (MultiFair) We propose a generic end-to-end algorithmic framework to solve it. Our proposed framework is generalizable to many different settings.
  arXiv  Detail & Related papers  (2021-05-24T02:30:22Z)
• Fair Mixup: Fairness via Interpolation [28.508444261249423]
  We propose fair mixup, a new data augmentation strategy for imposing the fairness constraint. We show that fairness can be achieved by regularizing the models on paths of interpolated samples between the groups. We empirically show that it ensures a better generalization for both accuracy and fairness measurement in benchmarks.
  arXiv  Detail & Related papers  (2021-03-11T06:57:26Z)
• Fairness for Whom? Understanding the Reader's Perception of Fairness in Text Summarization [9.136419921943235]
  We study the interplay between the fairness notions and how readers perceive them in textual summaries. Standard ROUGE evaluation metrics are unable to quantify the perceived (un)fairness of the summaries.
  arXiv  Detail & Related papers  (2021-01-29T05:14:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.