Extractive Summarization

Extractive summarization aims to create concise summaries by selecting the most important sentences from a longer text, without modification or paraphrasing. Current research focuses on adapting large language models (LLMs) for this task, overcoming limitations in context window size through multi-level frameworks and incorporating techniques like voting mechanisms to improve robustness. This approach shows promise for efficiently handling long documents, such as scientific papers and user-generated content, and improving applications like document question answering, ultimately enhancing information access and analysis.