Understanding Position Bias Effects on Fairness in Social Multi-Document Summarization

arXiv:2405.01790v1 Announce Type: new
Abstract: Text summarization models have typically focused on optimizing aspects of quality such as fluency, relevance, and coherence, particularly in the context of news articles. However, summarization models are increasingly being used to summarize diverse sources of text, such as social media data, that encompass a wide demographic user base. It is thus crucial to assess not only the quality of the generated summaries, but also the extent to which they can fairly represent the opinions …

abstract articles arxiv bias context cs.ai cs.cl data diverse document effects fairness however media media data quality social social media social media data summarization text text summarization type understanding