@inproceedings{tao-etal-2025-saki,
    title = "{SAKI}-{RAG}: Mitigating Context Fragmentation in Long-Document {RAG} via Sentence-level Attention Knowledge Integration",
    author = "Tao, Wenyu  and
      Xing, Xiaofen  and
      Li, Zeliang  and
      Xu, Xiangmin",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/automate-pypi-publishing/2025.emnlp-main.63/",
    doi = "10.18653/v1/2025.emnlp-main.63",
    pages = "1195--1213",
    ISBN = "979-8-89176-332-6",
    abstract = "Traditional Retrieval-Augmented Generation (RAG) frameworks often segment documents into larger chunks to preserve contextual coherence, inadvertently introducing redundant noise. Recent advanced RAG frameworks have shifted toward finer-grained chunking to improve precision. However, in long-document scenarios, such chunking methods lead to fragmented contexts, isolated chunk semantics, and broken inter-chunk relationships, making cross-paragraph retrieval particularly challenging. To address this challenge, maintaining granular chunks while recovering their intrinsic semantic connections, we propose \textbf{SAKI-RAG} (Sentence-level Attention Knowledge Integration Retrieval-Augmented Generation). Our framework introduces two core components: (1) the \textbf{SentenceAttnLinker}, which constructs a semantically enriched knowledge repository by modeling inter-sentence attention relationships, and (2) the \textbf{Dual-Axis Retriever}, which is designed to expand and filter the candidate chunks from the dual dimensions of semantic similarity and contextual relevance. Experimental results across four datasets{---}Dragonball, SQUAD, NFCORPUS, and SCI-DOCS demonstrate that SAKI-RAG achieves better recall and precision compared to other RAG frameworks in long-document retrieval scenarios, while also exhibiting higher information efficiency."
}