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 represents the majority opinions will leave the minority opinions unrepresented in the summary. In this paper, we use the stance towards a certain target as an opinion. We study bias in opinion summarisation from the perspective of opinion diversity, which measures whether the model generated summary can cover a diverse set of opinions. In addition, we examine opinion similarity, a measure of how closely related two opinions are in terms of their stance on a given topic, and its relationship with opinion diversity. Through the lens of stances towards a topic, we examine opinion diversity and similarity using three debatable topics under COVID-19. Experimental results on these topics revealed that a higher degree of similarity of opinions did not indicate good diversity or fairly cover the various opinions originally presented in the source documents. We found that BART and ChatGPT can better capture diverse opinions presented in the source documents.

翻译：意见摘要是一项旨在浓缩源文档信息、同时保留核心观点与意见的任务。若摘要仅代表多数意见，则少数意见将无法在摘要中得到呈现。本文将针对特定目标的立场视为一种意见，从意见多样性视角研究意见摘要中的偏见问题。意见多样性衡量模型生成的摘要是否能够覆盖多样化的意见集合。此外，我们考察了意见相似性——即两个意见在特定主题立场上的关联程度——及其与意见多样性之间的关系。通过主题立场的透镜，我们选取新冠疫情下的三个争议性主题，对其意见多样性与相似性展开分析。实验结果表明，意见相似性较高并不必然意味着良好的多样性或能公平覆盖源文档中的各类原始意见。研究发现BART与ChatGPT在捕捉源文档中的多元意见方面表现更优。