Query Focused
Query-focused summarization (QFS) aims to generate concise summaries of text that directly address a specific user query, improving information access and personalization. Current research heavily utilizes large language models (LLMs), often within retrieval-augmented generation (RAG) frameworks or incorporating techniques like contrastive learning and reinforcement learning to enhance summary relevance and accuracy. This field is significant due to its broad applications in search engines, report generation, and question answering systems, driving advancements in both natural language processing and information retrieval.
Papers
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