@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T ReTAG: Retrieval-Enhanced, Topic-Augmented Graph-Based Global Sensemaking
%A Kim, Boyoung
%A Lee, Dosung
%A An, Sumin
%A Jeong, Jinseong
%A Seo, Paul Hongsuck
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F kim-etal-2025-retag
%X 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.
%U https://aclanthology.org/2025.findings-emnlp.1212/
%P 22249-22277
Markdown (Informal)
[ReTAG: Retrieval-Enhanced, Topic-Augmented Graph-Based Global Sensemaking](https://aclanthology.org/2025.findings-emnlp.1212/) (Kim et al., Findings 2025)
ACL