@inproceedings{min-etal-2026-timelineagent,
title = "{T}imeline{A}gent: An {LLM}-based Framework for Query-Centric Event Timeline Extraction Across Multiple Documents",
author = "Min, Qingkai and
Bao, Yunxin and
Lu, Yifeng and
Feng, Zhenyu and
Chen, Yulong and
Vlachos, Andreas and
Zhang, Yue",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2328/",
pages = "52872--52896",
ISBN = "979-8-89176-459-0",
abstract = "Query-centric cross-document event timeline extraction aims to construct event timelines from multiple documents under diverse query focuses. The task is very challenging due to the heterogeneous event modeling and reasoning requirements arising from both the intrinsic complexity of cross-document timeline construction and the diversity of query focuses. In this paper, we propose TimelineAgent, an LLM-based multi-agent framework featuring a multi-stage reasoning workflow, where heterogeneous event features are organized into reusable skills and orchestrated through a goal-oriented protocol. To facilitate evaluation, we construct a benchmark covering diverse query focuses. Experimental results show that TimelineAgent consistently outperforms existing methods, offering a practical framework for complex real-world timeline construction."
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<abstract>Query-centric cross-document event timeline extraction aims to construct event timelines from multiple documents under diverse query focuses. The task is very challenging due to the heterogeneous event modeling and reasoning requirements arising from both the intrinsic complexity of cross-document timeline construction and the diversity of query focuses. In this paper, we propose TimelineAgent, an LLM-based multi-agent framework featuring a multi-stage reasoning workflow, where heterogeneous event features are organized into reusable skills and orchestrated through a goal-oriented protocol. To facilitate evaluation, we construct a benchmark covering diverse query focuses. Experimental results show that TimelineAgent consistently outperforms existing methods, offering a practical framework for complex real-world timeline construction.</abstract>
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%0 Conference Proceedings
%T TimelineAgent: An LLM-based Framework for Query-Centric Event Timeline Extraction Across Multiple Documents
%A Min, Qingkai
%A Bao, Yunxin
%A Lu, Yifeng
%A Feng, Zhenyu
%A Chen, Yulong
%A Vlachos, Andreas
%A Zhang, Yue
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F min-etal-2026-timelineagent
%X Query-centric cross-document event timeline extraction aims to construct event timelines from multiple documents under diverse query focuses. The task is very challenging due to the heterogeneous event modeling and reasoning requirements arising from both the intrinsic complexity of cross-document timeline construction and the diversity of query focuses. In this paper, we propose TimelineAgent, an LLM-based multi-agent framework featuring a multi-stage reasoning workflow, where heterogeneous event features are organized into reusable skills and orchestrated through a goal-oriented protocol. To facilitate evaluation, we construct a benchmark covering diverse query focuses. Experimental results show that TimelineAgent consistently outperforms existing methods, offering a practical framework for complex real-world timeline construction.
%U https://aclanthology.org/2026.emnlp-main.2328/
%P 52872-52896
Markdown (Informal)
[TimelineAgent: An LLM-based Framework for Query-Centric Event Timeline Extraction Across Multiple Documents](https://aclanthology.org/2026.emnlp-main.2328/) (Min et al., EMNLP 2026)
ACL