Summary Based Retrieval Task

Summary-based retrieval focuses on improving information retrieval by generating concise summaries of retrieved documents, enhancing the accuracy and efficiency of downstream tasks like question answering and open-domain question answering with LLMs. Current research emphasizes developing task-adaptive retrieval models, often employing techniques like instruction tuning, multi-task learning, and joint reranking-truncation models to optimize both the selection and presentation of retrieved information. These advancements are significant because they enable more effective and efficient information access for various applications, including improved search engines and enhanced capabilities for large language models.