EquiSumm : A Gender Bias-Aware Framework for Inclusive Tweet Summarization

2026-05-22

AI Summary

This work addresses the lack of demographic fairness—particularly with respect to gender—in existing methods for automatic tweet summarization, which often leads to biased representation of viewpoints. To mitigate this issue, the paper proposes the first framework that explicitly incorporates gender awareness into social media summarization by modeling perspective expression across different gender groups. The approach integrates gender identification with fairness-aware constraints to enhance gender representativeness in generated summaries. Experimental results on two widely used datasets demonstrate that the proposed method significantly improves the balanced representation of perspectives from diverse gender groups while preserving summary quality, thereby enabling more inclusive event summarization.

Abstract

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.

Problem

• gender bias
• tweet summarization
• demographic fairness
• inclusive summarization
• social media

Innovation

• gender bias
• inclusive summarization
• social media
• fairness-aware NLP
• tweet summarization