Fair Summarization: Bridging Quality and Diversity in Extractive Summaries
Sina Bagheri Nezhad, Sayan Bandyapadhyay, Ameeta Agrawal·November 12, 2024
Summary
The paper introduces FairExtract and FairGPT for fair extractive summarization, addressing bias in multi-document summarization of user-generated content. It uses the Divsumm dataset to evaluate these methods against baselines, showing superior fairness while maintaining competitive quality. The work highlights the importance of fairness in summarization, offering a benchmark for future research in fairness-aware NLP models.
Introduction
Background
Overview of extractive summarization
Importance of fairness in summarization
Challenges in multi-document summarization of user-generated content
Objective
To introduce FairExtract and FairGPT for fair extractive summarization
To evaluate these methods against baselines using the Divsumm dataset
To demonstrate superior fairness while maintaining competitive quality
Method
Data Collection
Source of Divsumm dataset
Characteristics of the dataset
Data Preprocessing
Preprocessing steps for the Divsumm dataset
Handling biases in the dataset
Model Architecture
Overview of FairExtract and FairGPT
Key components and mechanisms for fairness
Training and Evaluation
Training process of FairExtract and FairGPT
Evaluation metrics used
Comparison with baselines
Results
Fairness Analysis
Metrics for assessing fairness
Comparison of FairExtract and FairGPT with baselines
Quality Assessment
Metrics for summarization quality
Comparison of FairExtract and FairGPT with baselines
Comparative Analysis
Fairness vs. quality trade-off
Insights from the results
Discussion
Implications for Fairness in NLP
Importance of fairness in NLP models
FairExtract and FairGPT as benchmarks
Future Directions
Research opportunities in fairness-aware NLP
Potential improvements for FairExtract and FairGPT
Conclusion
Summary of Contributions
Key findings and contributions of the paper
Impact and Applications
Potential impact on fairness in multi-document summarization
Applications of FairExtract and FairGPT in real-world scenarios
Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
What is the main contribution of the paper regarding fair extractive summarization?
How does the paper evaluate the performance of FairExtract and FairGPT?
What dataset is used to assess the methods against baselines in the paper?
What does the paper emphasize about the importance of fairness in summarization tasks?