Multi Document Summarisation

Multi-document summarization (MDS) aims to condense information from multiple sources into a concise and coherent summary, addressing the growing need to efficiently process large volumes of textual data. Current research focuses on improving the accuracy and fluency of summaries using various deep learning models, including transformer-based architectures like BART and T5, often enhanced by techniques such as prompt engineering and the incorporation of domain-specific ontologies. This field is crucial for applications ranging from healthcare (e.g., summarizing patient records) to scientific literature review, promising to significantly improve information access and analysis across diverse domains.