Adapter-Based Multi-Document Summarisation: Opinion Summarisation Use Case

K. Hewapathirana, N. de Silva, and C. Athuraliya Proceedings of the 18th International Conference on Agents and Artificial Intelligence - Volume 4: ICAART, INSTICC, 2026, pp. 3018-3027, This study explores adapter-based fine-tuning to enhance the PRIMERA model for opinion summarisation. PRIMERA, a state-of-the-art multi-document summarisation (MDS) model, exhibits strong transfer potential owing to its pre-training on large-scale MDS corpora. Leveraging adapter architectures, this work demonstrates substantial improvements when extending PRIMERA to opinion summarisation through parameter-efficient fine-tuning. In addition, an LLM-based evaluation paradigm is introduced using the DeepEval framework, enabling semantic and sentiment-aware assessment beyond lexical-overlap metrics such as ROUGE. To improve training efficiency, an agentic optimisation framework is proposed, where evaluation reasoning guides iterative adapter configuration, reducing fine-tuning cycles while maintaining performance. Results show that adapter-augmented PRIMERA surpasses the opinion summarisation baseline ADASUM, establishing a reproducible, interpretable, and computationally efficient path for low-resource MDS. Overall, this work highlights how adapter-based fine-tuning and reasoning-guided optimisation together advance both performance and applicability in opinion summarisation.