Summary of Understanding Position Bias Effects on Fairness in Social Multi-document Summarization, by Olubusayo Olabisi and Ameeta Agrawal
Understanding Position Bias Effects on Fairness in Social Multi-Document Summarization
by Olubusayo Olabisi, Ameeta Agrawal
First submitted to arxiv on: 3 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a study on position bias in text summarization models when applied to diverse social media datasets. In news summarization, models focus on quality aspects like fluency and coherence, but in social media, they need to fairly represent opinions from various groups. The authors investigate this phenomenon by analyzing the effect of group ordering in input documents when summarizing tweets from three linguistic communities: African-American English, Hispanic-aligned Language, and White-aligned Language. They find that although summary quality remains consistent regardless of input order, fairness varies significantly depending on how dialect groups are presented. This study highlights the importance of considering position bias in social multi-document summarization to ensure fair representation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a problem with machines that summarize text from social media. These machines are usually good at making summaries sound nice and clear, but they don’t always do a great job of showing what different groups think. The authors looked at how these machines do when they’re given tweets from three different language communities: African-American English, Hispanic-aligned Language, and White-aligned Language. They found that even though the summaries themselves are fine no matter how the input is ordered, the way different groups’ opinions are represented changes a lot depending on how those groups are presented in the input data. This shows us that we need to think about this problem of position bias when using machines to summarize text from social media. |
Keywords
» Artificial intelligence » Summarization
