ReTAG: Retrieval-Enhanced, Topic-Augmented Graph-Based Global Sensemaking
Boyoung Kim, Dosung Lee, Sumin An, Jinseong Jeong, Paul Hongsuck Seo
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.
BibTeX
@inproceedings{emnlp2025_retagretrievalen,
title = {ReTAG: Retrieval-Enhanced, Topic-Augmented Graph-Based Global Sensemaking},
author = {Boyoung Kim and Dosung Lee and Sumin An and Jinseong Jeong and Paul Hongsuck Seo},
booktitle = {EMNLP 2025},
year = {2025}
}