@inproceedings{kim-etal-2025-retag,
    title = "{R}e{TAG}: Retrieval-Enhanced, Topic-Augmented Graph-Based Global Sensemaking",
    author = "Kim, Boyoung  and
      Lee, Dosung  and
      An, Sumin  and
      Jeong, Jinseong  and
      Seo, Paul Hongsuck",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.1212/",
    doi = "10.18653/v1/2025.findings-emnlp.1212",
    pages = "22249--22277",
    ISBN = "979-8-89176-335-7",
    abstract = "Recent advances in question answering have led to substantial progress in tasks such as multi-hop reasoning. However, global sensemaking{---}answering questions by synthesizing information from an entire corpus{---}remains a significant challenge. A prior graph-basedapproach to global sensemaking lacks retrieval mechanisms, topic specificity, and incurs high inference costs. To address these limitations, we propose ReTAG, a RetrievalEnhanced, Topic-Augmented Graph framework that constructs topic-specific subgraphs and retrieves the relevant summaries for response generation. Experiments show that ReTAG improves response quality while significantly reducing inference time compared to the baseline. Our code is available at https://github.com/bykimby/retag."
}