IDEAL: Leveraging Infinite and Dynamic Characterizations of Large Language Models for Query-focused Summarization
arXiv:2407.10486v3 Announce Type: replace-cross Abstract: Query-focused summarization (QFS) aims to produce summaries that answer particular questions of interest, enabling greater user control and personalization. The advent of large language models (LLMs), shows their impressive capability of textual understanding through large-scale pretraining, which implies the great potential of extractive snippet generation. In this paper, we systematically investigated two indispensable characteristics that the LLMs-based QFS models should be harnessed, \emph{Efficiently Fine-grained Query-LLM Alignmen
The rapid advancement and widespread adoption of large language models (LLMs) make their refinement for practical applications like query-focused summarization a critical and immediate research focus.
Improving LLM-based query-focused summarization will significantly enhance user control and personalization in information retrieval, making data more accessible and tailored.
The ability to efficiently extract precise information from vast text corpuses based on specific user queries will improve LLM utility, transitioning them from general understanding to targeted application.
- · AI researchers
- · Information retrieval platforms
- · Knowledge management sectors
- · SaaS providers leveraging LLMs
- · Generic search engines
- · Manual summarization services
- · Users without specific query capabilities
Enhanced query-focused summarization improves the efficiency and personalization of information access.
Better summarization tools could lead to more nuanced and rapid decision-making in various industries by quickly extracting critical insights.
The widespread implementation of highly personalized information access via QFS could reshape how individuals and organizations consume and interact with digital content, fostering new data interfaces.
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