Summary of Fair Summarization: Bridging Quality and Diversity in Extractive Summaries, by Sina Bagheri Nezhad et al.
Fair Summarization: Bridging Quality and Diversity in Extractive Summaries
by Sina Bagheri Nezhad, Sayan Bandyapadhyay, Ameeta Agrawal
First submitted to arxiv on: 12 Nov 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 proposed methods for fair extractive summarization, FairExtract and FairGPT, aim to address the issue of biased outputs in natural language processing (NLP) by introducing fairness constraints. The novel approaches are evaluated using a comprehensive set of metrics, including SUPERT, BLANC, SummaQA, BARTScore, UniEval, and F, on the Divsumm dataset of user-generated content. The results show that FairExtract and FairGPT achieve superior fairness while maintaining competitive summarization quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Fairness in summarizing social media posts is important because current methods often ignore different groups’ voices. Two new ways to make summaries fair are proposed: FairExtract, which groups similar ideas together, and FairGPT, which uses a big language model with fairness rules. The new methods are tested on tweets from three social groups (White-aligned, Hispanic, and African-American) using various metrics that measure how good the summaries are and how fair they are. The results show that these new methods can make fair and good summaries. |
Keywords
» Artificial intelligence » Language model » Natural language processing » Nlp » Summarization
