@inproceedings{cao-etal-2025-neusym,
    title = "{N}eu{S}ym-{RAG}: Hybrid Neural Symbolic Retrieval with Multiview Structuring for {PDF} Question Answering",
    author = "Cao, Ruisheng  and
      Zhang, Hanchong  and
      Huang, Tiancheng  and
      Kang, Zhangyi  and
      Zhang, Yuxin  and
      Sun, Liangtai  and
      Li, Hanqi  and
      Miao, Yuxun  and
      Fan, Shuai  and
      Chen, Lu  and
      Yu, Kai",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.acl-long.311/",
    doi = "10.18653/v1/2025.acl-long.311",
    pages = "6211--6239",
    ISBN = "979-8-89176-251-0",
    abstract = "The increasing number of academic papers poses significant challenges for researchers to efficiently acquire key details. While retrieval augmented generation (RAG) shows great promise in large language model (LLM) based automated question answering, previous works often isolate neural and symbolic retrieval despite their complementary strengths. Moreover, conventional single-view chunking neglects the rich structure and layout of PDFs, e.g., sections and tables. In this work, we propose NeuSym-RAG, a hybrid neural symbolic retrieval framework which combines both paradigms in an interactive process. By leveraging multi-view chunking and schema-based parsing, NeuSym-RAG organizes semi-structured PDF content into both the relational database and vectorstore, enabling LLM agents to iteratively gather context until sufficient to generate answers. Experiments on three full PDF-based QA datasets, including a self-annotated one AirQA-Real, show that NeuSym-RAG stably defeats both the vector-based RAG and various structured baselines, highlighting its capacity to unify both retrieval schemes and utilize multiple views."
}