Multi Document Summarization

Multi-document summarization (MDS) aims to automatically generate concise summaries from multiple related documents, addressing the challenge of information overload. Current research focuses on improving the accuracy and efficiency of MDS using large language models (LLMs), often incorporating techniques like extractive and abstractive methods, graph-based approaches, and hierarchical encoding-decoding schemes. These advancements are crucial for various applications, including news aggregation, scientific literature review, and enterprise information processing, enabling more efficient and effective information access and analysis. A key challenge remains ensuring fairness and mitigating biases, particularly when dealing with diverse sources like social media.