EquiSumm : A Gender Bias-Aware Framework for Inclusive Tweet Summarization
For social media analysts and media agencies, this work addresses the problem of gender bias in automated tweet summarization, though the improvement is incremental over existing summarization techniques.
EquiSumm is a gender bias-aware framework for tweet summarization that explicitly considers gender representation to produce more inclusive summaries. Experiments on two datasets show it outperforms existing methods in balancing performance and fairness.
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.