Automatic Summarization

Automatic summarization aims to condense large text documents into concise summaries while preserving key information. Current research heavily utilizes large language models (LLMs) and generative models, often incorporating techniques like prompt tuning and information-theoretic distillation to improve summary quality and address issues like hallucinations and bias. Focus areas include adapting these models to diverse domains (legal texts, medical dialogues, scientific papers) and languages, as well as developing more robust evaluation metrics beyond traditional measures like ROUGE scores. The field's advancements have significant implications for information access, knowledge management, and various applications requiring efficient text processing.