Multi-document news summarisation systems are increasingly adopted for their convenience in processing vast daily news content, making fairness across diverse political perspectives critical.However, these systems can exhibit political bias through unequal representation of viewpoints, disproportionate emphasis on certain perspectives, and systematic underrepresentation of minority voices.This study presents a comprehensive evaluation of such bias in multi-document news summarisation using FairNews, a dataset of complete news articles with political orientation labels, examining how large language models (LLMs) handle sources with varying political leanings across 13 models and five fairness metrics.We investigate both baseline model performance and effectiveness of various debiasing interventions, including promptbased and judge-based approaches.Our findings challenge the assumption that larger models yield fairer outputs, as mid-sized variants consistently outperform their larger counterparts, offering the best balance of fairness and efficiency.Prompt-based debiasing proves highly model dependent, while entity sentiment emerges as the most stubborn fairness dimension, resisting all intervention strategies tested.These results demonstrate that fairness in multidocument news summarisation requires multidimensional evaluation frameworks and targeted, architecture-aware debiasing rather than simply scaling up.

Model compression through post-training pruning offers a way to reduce model size and computational requirements without significantly impacting model performance.However, the effect of pruning on the fairness of LLMgenerated summaries remains unexplored, particularly for opinion summarisation where biased outputs could influence public views.In this paper, we present a comprehensive empirical analysis of opinion summarisation, examining three state-of-the-art pruning methods and various calibration sets across three opensource LLMs using four fairness metrics.Our systematic analysis reveals that pruning methods have a greater impact on fairness than calibration sets.Building on these insights, we propose High Gradient Low Activation (HGLA) pruning, which identifies and removes parameters that are redundant for input processing but influential in output generation.Our experiments demonstrate that HGLA can better maintain or even improve fairness compared to existing methods, showing promise across models and tasks where traditional methods have limitations.Our human evaluation shows HGLA-generated outputs are fairer than existing state-of-the-art pruning methods.

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 summaries that enable users to effectively understand the opinions therein.Generating biased summaries has the risk of potentially swaying public opinion.Previous studies focused on studying bias in opinion summarisation using extractive models, but limited research has paid attention to abstractive summarisation models.In this study, using political bias as a case study, we first establish a methodology to quantify bias in abstractive models, then trace it from the pre-trained models to the task of summarising social media opinions using different models and adaptation methods.We find that most models exhibit intrinsic bias.Using a social media text summarisation dataset and contrasting various adaptation methods, we find that tuning a smaller number of parameters is less biased compared to standard fine-tuning; however, the diversity of topics in training data used for fine-tuning is critical.