3 papers
cs.CL2026
When Bigger Isn't Better: A Comprehensive Fairness Evaluation of Political Bias in Multi-News Summarisation
Nannan Huang, Iffat Maab, Junichi Yamagishi
Multi-document news summarisation systems are increasingly adopted for their convenience in processing vast daily news content, making fairness across diverse political perspective…
cs.CL2025
REFER: Mitigating Bias in Opinion Summarisation via Frequency Framed Prompting
Nannan Huang, Haytham M. Fayek, Xiuzhen Zhang
Individuals express diverse opinions, a fair summary should represent these viewpoints comprehensively. Previous research on fairness in opinion summarisation using large language…
cs.CL2025
Less Is More? Examining Fairness in Pruned Large Language Models for Summarising Opinions
Nannan Huang, Haytham M. Fayek, Xiuzhen Zhang
Model compression through post-training pruning offers a way to reduce model size and computational requirements without significantly impacting model performance. However, the eff…