Examining Bias in Opinion Summarisation Through the Perspective of
  Opinion Diversity
Nannan Huang Lin Tian Haytham Fayek Xiuzhen Zhang
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.