@inproceedings{liang-etal-2025-explain,
    title = "{EXPLAIN}: Enhancing Retrieval-Augmented Generation with Entity Summary",
    author = "Liang, Yaozhen  and
      Liu, Xiao  and
      Yu, Jiajun  and
      Fang, Zhouhua  and
      Zou, Qunsheng  and
      Zheng, Linghan  and
      Li, Yong  and
      Liu, Zhiwei  and
      Wang, Haishuai",
    editor = "Rehm, Georg  and
      Li, Yunyao",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.acl-industry.108/",
    pages = "1520--1529",
    ISBN = "979-8-89176-288-6",
    abstract = "Document question answering plays a crucial role in enhancing employee productivity by providing quick and accurate access to information. Two primary approaches have been developed: retrieval-augmented generation (RAG), which reduces input tokens and inference costs, and long-context question answering (LC), which processes entire documents for higher accuracy. We introduce EXPLAIN (\textbf{EX}tracting, \textbf{P}re-summarizing, \textbf{L}inking and enh\textbf{A}c\textbf{IN}g RAG), a novel retrieval-augmented generation method that automatically extracts useful entities and generates summaries from documents. EXPLAIN improves accuracy by retrieving more informative entity summaries, achieving precision comparable to LC while maintaining low token consumption. Experimental results on internal dataset (ROUGE-L from 30.14{\%} to 30.31{\%}) and three public datasets (HotpotQA, 2WikiMQA, and Quality, average score from 62{\%} to 64{\%}) demonstrate the efficacy of EXPLAIN. Human evaluation in ant group production deployment indicates EXPLAIN surpasses baseline RAG in comprehensiveness."
}