Advanced multiple document summarization via iterative recursive transformer networks and multimodal transformer

Abstract

The proliferation of digital information necessitates advanced techniques for multiple document summarization, capable of distilling vast textual data efficiently. Traditional approaches often struggle with coherence, integration of multimodal data, and suboptimal learning strategies. To address these challenges, this work introduces novel neural architectures and methodologies. At its core is recursive transformer networks (ReTran), merging recursive neural networks with transformer architectures for superior comprehension of textual dependencies, projecting a 5–10% improvement in ROUGE scores. Cross-modal summarization employs a multimodal transformer with cross-modal attention, amalgamating text, images, and metadata for more holistic summaries, expecting an 8 to 12% enhancement in quality metrics. Actor-critic reinforcement learning refines training by optimizing summary quality, surpassing Q-learning-based strategies by 5–8%. Meta-learning for zero-shot summarization addresses summarizing unseen domains, projecting a 6–10% uptick in performance. Knowledge-enhanced transformer integrates external knowledge for improved semantic coherence, potentially boosting ROUGE scores by 7 to 12%. These advancements not only improve numerical performance but also produce more informative and coherent summaries across diverse domains and modalities. This work represents a significant stride in multiple document summarization, setting a new benchmark for future research and applications.