Human Written Summary

Human-written text summarization research aims to understand and replicate the process of creating concise, informative summaries from longer texts, encompassing diverse formats like videos, legal judgments, and social media conversations. Current research focuses on improving automatic summarization models, often employing deep learning architectures like transformers (e.g., BART, T5) and incorporating techniques such as conditional modeling, multi-modal fusion, and reinforcement learning to generate more accurate, coherent, and personalized summaries. This work is significant for improving information access and retrieval across various domains, impacting fields ranging from legal research and disaster response to clinical practice and scientific literature review.