Domain-Specific Multi-Document Political News Summarization Using BART and ACT-GAN

The exponential growth of digital content has made it increasingly difficult for users to understand the information, particularly in domains like political news. The study provides a framework for domain-specific multi-document political news summarization by using the transformer-based models that are fine-tuned on a synthetic dataset. The use of a generative Artificial Intelligence (AI) model, specifically the Bidirectional and Autoregressive Transformer (BART) model, for summarizing multi-document political news was explored in this paper. To improve the summarization process, real-world data was augmented, and a synthetic dataset was generated using the Adversarial Contextualized Text-Generative Adversarial Network (ACT-GAN) model to create a diverse and domain-specific training dataset. All four models, including BART, Pegasus, Text-To-Text Transfer Transformer (T5), and Sequence-to-Sequence (Seq2Seq), were fine-tuned and evaluated using a synthetic dataset generated via ACT-GAN and benchmarked using Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. BART performs the best in accuracy, recall, and F1-score, showing it can create clear and logical summaries while keeping the important context and structure intact. The results show that the transformer with the synthetic data helps to improve the quality of the political data summarization. In addition to addressing the challenges of extensive information, this framework also employs generative AI models in text summarization domains.