从观点多样性视角考察观点摘要中的偏见
Examining Bias in Opinion Summarisation Through the Perspective of Opinion Diversity
AI总结：
本文以对主题的立场界定观点，利用 COVID-19 下三个争议主题评估观点摘要的多样性与相似性，发现高相似性不代表高覆盖度，而 BART 和 ChatGPT 更能捕捉多元观点。
摘要：
观点摘要是一项旨在压缩源文档所呈现信息，同时保留核心信息和观点的任务。只代表多数观点的摘要会使少数观点在摘要中得不到体现。本文将对某一特定对象的立场作为一种观点。我们从观点多样性的视角研究观点摘要中的偏见，该视角衡量模型生成的摘要是否能够覆盖多样化的观点集合。此外，我们考察了观点相似性，即衡量两种观点在给定主题上的立场相关程度，及其与观点多样性之间的关系。通过对某一主题的立场这一视角，我们使用 COVID-19 背景下三个具有争议性的主题来考察观点多样性和相似性。在这些主题上的实验结果表明，较高的观点相似程度并不意味着良好的多样性，也不能公平覆盖源文档中最初呈现的各种观点。我们发现，BART 和 ChatGPT 能够更好地捕捉源文档中呈现的多样化观点。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
