让推文摘要更公平：考虑性别差异，避免观点偏倚
EquiSumm : A Gender Bias-Aware Framework for Inclusive Tweet Summarization
- 引入性别感知框架，平衡不同性别的观点表达
- 在两个数据集上验证，相比旧方法更少性别偏见
- 适合关注社会公平与可解释性的人工智能研究者
社交媒体平台（如 Twitter）在新闻事件中为大规模意见分享提供了渠道，但人工难以处理海量内容以提取关键观点。为此，已有自动摘要技术将大量推文浓缩为简明信息。然而，这些算法未显式考虑人口统计公平性。现有方法虽能概括主要观点，却常忽略性别等维度的代表性，导致摘要存在偏见。本文提出 EquiSumm 框架，显式纳入性别因素生成更包容的摘要。在两个主流数据集上的实验表明，该方法在减少性别偏差方面优于现有工作。
原文摘要 · Abstract (English)
While social media platforms, such as Twitter, provide a medium for large-scale opinion sharing during news events, it is manually impossible for individuals or media agencies to process the vast volume of content to identify key viewpoints. In order to resolve this, several automatic summarization techniques have been proposed to condense large collections of tweets into concise and informative summaries. However, these algorithms do not explicitly consider demographic fairness. Several existing research works have developed automated summarization approaches that can provide a holistic overview of the key aspects and major opinions shared on social media platforms related to a news event. However, these approaches do not explicitly consider different forms of demographic representation, such as gender, which can lead to biased summary representation. In this paper, we propose EquiSumm, which considers the gender aspect of the shared opinion to generate a summary, and our experimental analysis on two major datasets indicates the performance effectiveness with respect to existing research works.
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