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

Core Concepts

Position bias in summarization models can severely impact the fairness of generated summaries, especially when summarizing diverse social data.

Abstract

The paper investigates the phenomenon of position bias in the context of social multi-document summarization. The key findings are: When the input documents are presented in a randomly shuffled order, the summarization models do not exhibit any notable position bias, neither in human-written reference summaries nor in system-generated summaries. However, when the input documents are ordered based on the dialect groups they belong to, the summarization models show a significant position bias, favoring the group whose documents appear first in the input. This position bias in ordered inputs has a severe impact on the fairness of the generated summaries, with the group whose documents appear first being significantly over-represented compared to the other groups. In contrast, the shuffled input leads to more balanced summaries across all groups. Interestingly, the textual quality of the summaries, as measured by standard metrics like ROUGE, BARTScore, BERTScore, and UniEval, remains largely consistent regardless of whether the input is ordered or shuffled. The findings highlight the importance of considering fairness, in addition to textual quality, when evaluating summarization models, especially in the context of diverse social data. The results suggest that introducing randomness in the presentation of input data can help mitigate the position bias and improve the fairness of the generated summaries.

Stats

The average token overlap between the system-generated summaries and each document in the input set is used to quantify position bias.

Quotes

None

Key Insights Distilled From

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

by Olubusayo Ol... at arxiv.org 05-06-2024

https://arxiv.org/pdf/2405.01790.pdf

Deeper Inquiries

How can we design summarization models that are inherently robust to position bias and ensure fair representation of diverse social groups in the generated summaries?

To design summarization models that are inherently robust to position bias and ensure fair representation of diverse social groups, several strategies can be implemented: Diverse Training Data: Ensure that the training data for the summarization models is diverse and representative of the various social groups whose content will be summarized. This can help the model learn to generate summaries that encompass a wide range of perspectives. Randomization: Introduce randomness in the presentation of input data to the model. By shuffling the order of documents from different social groups, the model is less likely to develop biases towards specific groups based on their position in the input sequence. Fairness Metrics: Incorporate fairness metrics into the evaluation of the summarization models. These metrics can help quantify the extent to which the generated summaries represent the diversity of the input data and identify any biases towards specific groups. Bias Mitigation Techniques: Implement techniques such as debiasing algorithms or adversarial training to mitigate biases in the model's decision-making process. These techniques can help ensure that the model does not favor certain groups over others in the summarization process. Post-Processing Analysis: Conduct post-processing analysis of the generated summaries to identify and rectify any biases or unfair representations. This can involve manual review or the use of additional algorithms to ensure the summaries are balanced and inclusive. By incorporating these strategies, summarization models can be designed to be more robust to position bias and provide fair representation of diverse social groups in the generated summaries.