A dv S umm: Adversarial Training for Bias Mitigation in Text Summarization (2025)

Abstract: Large Language Models (LLMs) have achieved impressive performance in text summarization and are increasingly deployed in real-world applications. However, these systems often inherit associative and framing biases from pre-training data, leading to inappropriate or unfair outputs in downstream tasks. In this work, we present AdvSumm (Adversarial Summarization), a domain-agnostic training framework designed to mitigate bias in text summarization through improved generalization. Inspired by adversarial robustness, AdvSumm introduces a novel Perturber component that applies gradient-guided perturbations at the embedding level of Sequence-to-Sequence models, enhancing the model’s robustness to input variations. We empirically demonstrate that AdvSumm effectively reduces different types of bias in summarization—specifically, name-nationality bias and political framing bias—without compromising summarization quality. Compared to standard transformers and data augmentation techniques like back-translation, AdvSumm achieves stronger bias mitigation performance across benchmark datasets.

## introduction large language models (llms) have achieved impressive performances in text generation tasks, including summarization @xcite . as a result, llms are being integrated into real-world applications. for example, social media platforms use them to generate personalized feed summaries based on user preferences @xcite ; search engines provide direct summaries of relevant documents in response to user queries 1 ; and enterprise solutions employ them to summarize meeting transcripts, and emails 2 , among other use cases. however, prior research has shown that these systems often inherit biases from their pretraining data (hovy 1 perplexity ai 2 microsoft copilot for sales and prabhumoye, 2021; @xcite @xcite , which can pose serious threats in downstream tasks. as shown in figure 1 , summaries generated by existing systems can exhibit various forms of bias. for example, they may contain associative biases @xcite , which reflect preferences or prejudices toward certain groups, or framing biases @xcite , which convey implicit political leanings. most prior work on bias mitigation relies on domain-specific strategies, such as expert interventions @xcite , curated word lists @xcite , or the collection of additional data to improve population representation. these approaches are often expensive and do not generalize well across different types of bias. moreover, most domain-agnostic bias mitigation techniques have been developed for classification tasks (e.g., employing risk minimization methods @xcite across different target groups @xcite ). however, these methods are not scalable to text generation tasks, where bias may arise from the selection of multiple tokens rather than a single output label. this highlights the need for bias mitigation strategies for text generation models that are independent of particular domains or forms of bias. given the generalization limitations of existing bias mitigation frameworks in text generation, we propose advsumm: adversarial summarization. our approach integrates a domain-agnostic component, perturber, into the model training process to reduce multiple forms of bias in generated summaries. we reformulate bias reduction in text summarization as a generalization problem that can be addressed by enhancing the model's robustness to input perturbations @xcite . prior work on adversarial training @xcite across applications has shown its effectiveness in improving robustness. it is unclear, however, how adversarial training can be applied to language generation tasks. building on this, we introduce an adversarial training strategy designed to mitigate biases originating from pre-training data by improving model robustness during fine-tuning. while other fields have benefited from adversarial robustness, it is difficult to apply to natural language due to the discrete nature of text data, unlike continuous modalities such as images or speech. we adopt adversarial training by introducing perturbations with the perturber component at the embedding level of sequence-to-sequence (seq2seq) models @xcite . as illustrated in figure 2 , the perturber takes in the continuous embedding from the transformer encoder, generates an adversarial embedding, and pushes the decoder output towards the same ground truth summary. this adversarial embedding helps improve robustness during training. compared to baseline methods, our approach shows reductions in bias metrics while retaining the summarization quality. advsumm is designed to generalize across multiple types of bias. in this work, we demonstrate empirical improvements in mitigating two specific forms of bias: name-nationality bias @xcite and political framing bias @xcite . our key contributions are as follows: generalization for bias mitigation. research in computer vision has explored contrastive learning strategies for domain transfer @xcite and improved generalization @xcite , both of which also have potential implications for bias mitigation. @xcite , for instance, highlights a connection between model robustness and biases in facial recognition tasks. in the context of text generation, data augmentation techniques have been widely adopted for improving robustness @xcite and faithfulness in summarization @xcite . closest to our work, frsum @xcite and advseq @xcite show how inducing robustness in language generation models encourages faithfulness on summarization tasks. we extend this line of research by introducing adversarial training for robustness specifically targeted at bias mitigation. we demonstrate that our proposed method outperforms back-translation-based data augmentation on bias mitigation benchmarks. another line of prior work focuses on reducing model bias through empirical risk minimization (erm) and invariant risk minimization (irm) (arjovsky et al., 2020), both of which aim to enhance generalization across samples from different target groups @xcite . these methods, however effective, typically require expert-labeled subgroup annotations, limiting their scalability in practice. our approach builds upon these frameworks, proposing a domain-agnostic adversarial training strategy specifically designed to mitigate biases in text summarization. to our knowledge, we are the first to adapt adversarial training effectively for bias mitigation in sequence-to-sequence text generation models, providing a scalable and generalized solution across multiple types of bias @xcite 3 methods ## results we present our empirical findings on the two types of biases in this section. ## conclusions in this work, we introduced advsumm, a domainagnostic adversarial training framework for bias mitigation in text summarization. motivated by the limitations of existing bias mitigation strategies-particularly their domain-specific nature and difficulty generalizing across different types of biases-we reformulated bias reduction as a generalization problem, tackled through adversarial robustness. by introducing the perturber mod-ule to apply embedding-level adversarial perturbations during fine-tuning, we demonstrated that ad-vsumm effectively reduces both token-level biases (e.g., name-nationality associations) and documentlevel biases (e.g., political framing) without compromising summarization quality. empirical results on benchmark datasets highlight that adv-summ outperforms standard transformers and backtranslation baselines, offering a unified and scalable solution for fairer text generation.