HiRAG: A Historical Information-Driven Retrieval-Augmented Generation Framework for Background Summarization
Keywords: Retrieval Augmented Generation, Background Summarization, Similarity, Sliding Window Mechanism
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Abstract: In an era overwhelmed by a deluge of global information, it is often challenging for people to grasp the relationships that an event develops over time. The background summarization (BS) task facilitates a profound understanding of the relationships between the current background of an event at any given time and its historical backgrounds. To enhance comprehension and help news readers and professionals to quickly understand the evolution of events, we introduce a Historical information-driven Retrieval-Augmented Generation framework (HiRAG). This framework is designed to extract the most relevant information from historical backgrounds and supplement it to generate precise background summarization. HiRAG employs state-of-the-art retrieval-augmented generation technologies to produce relevant background summarization. We implement a multi-strategy similarity calculation and introduce a sliding window mechanism to optimize retrieval construction. Our framework has been rigorously tested through a series of experiments and extensive analyses of the latest datasets. The promising results affirm the effectiveness of our proposed HiRAG framework and its retrieval capabilities.
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Primary Area: Applications (bioinformatics, biomedical informatics, climate science, collaborative filtering, computer vision, healthcare, human activity recognition, information retrieval, natural language processing, social networks, etc.)
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Submission Number: 303
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