Opinion Summarisation

Opinion summarization aims to condense multiple opinions on a topic into a concise, representative summary, focusing on accurately reflecting the range of viewpoints expressed. Current research emphasizes mitigating biases in summarization, particularly in abstractive models, by exploring techniques like careful model fine-tuning and incorporating measures of opinion diversity. This work leverages various architectures, including large language models, graph neural networks, and variational autoencoders, often employing unsupervised or weakly supervised learning methods. Improved opinion summarization techniques have significant implications for applications such as sentiment analysis, rumour verification, and enhancing online information access by providing more balanced and informative overviews of complex topics.