5 papers · 1 filter
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…
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…
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…
Bias in Opinion Summarisation from Pre-training to Adaptation: A Case Study in Political Bias
Nannan Huang, Haytham Fayek, Xiuzhen Zhang
Opinion summarisation aims to summarise the salient information and opinions presented in documents such as product reviews, discussion forums, and social media texts into short su…
Examining Bias in Opinion Summarisation Through the Perspective of Opinion Diversity
Nannan Huang, Lin Tian, Haytham Fayek +1
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 represent…