Advanced multiple document summarization <i>via</i> 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 qual

SUBMITTER: Ketineni S 

PROVIDER: S-EPMC11784779 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature