@inproceedings{zheng-etal-2026-gaps,
title = "From Gaps to Active Retrieval : Open-Domain Iterative Timeline Completion via Historical Trajectory Analogy and Dynamic Ontology",
author = "Zheng, Yunhui and
Bao, Chenlong and
Li, Shasha and
Wang, Ting and
Tang, Jin-Tao",
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.256/",
pages = "5809--5826",
ISBN = "979-8-89176-459-0",
abstract = "Open-Domain Timeline Summarization (Open-TLS) seeks to construct coherent evolutionary narratives for a topic by distilling information from large, unstructured web streams. Existing retrieve-and-generate pipelines often fail to capture logically necessary but semantically distant evidence, resulting in macro-level causal gaps (missing intermediate stages) and micro-level element gaps (underspecified milestones). We propose Active Timeline Completion (ATC), an iterative framework that converts implicit narrative gaps into targeted retrieval questions, enabling reflection-driven evidence acquisition. ATC leverages three complementary mechanisms{---}parametric causal gap detection, historical trajectory analogy, and dynamic ontology-guided completion{---}while a Logical Utility Verification module ensures only evidence directly addressing these gaps updates the timeline. Experiments on Open-TLS demonstrate that ATC consistently outperforms strong baselines in both content coverage and temporal alignment, showing that active, question-driven retrieval improves the completeness and coherence of open-domain timelines."
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<abstract>Open-Domain Timeline Summarization (Open-TLS) seeks to construct coherent evolutionary narratives for a topic by distilling information from large, unstructured web streams. Existing retrieve-and-generate pipelines often fail to capture logically necessary but semantically distant evidence, resulting in macro-level causal gaps (missing intermediate stages) and micro-level element gaps (underspecified milestones). We propose Active Timeline Completion (ATC), an iterative framework that converts implicit narrative gaps into targeted retrieval questions, enabling reflection-driven evidence acquisition. ATC leverages three complementary mechanisms—parametric causal gap detection, historical trajectory analogy, and dynamic ontology-guided completion—while a Logical Utility Verification module ensures only evidence directly addressing these gaps updates the timeline. Experiments on Open-TLS demonstrate that ATC consistently outperforms strong baselines in both content coverage and temporal alignment, showing that active, question-driven retrieval improves the completeness and coherence of open-domain timelines.</abstract>
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%0 Conference Proceedings
%T From Gaps to Active Retrieval : Open-Domain Iterative Timeline Completion via Historical Trajectory Analogy and Dynamic Ontology
%A Zheng, Yunhui
%A Bao, Chenlong
%A Li, Shasha
%A Wang, Ting
%A Tang, Jin-Tao
%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 zheng-etal-2026-gaps
%X Open-Domain Timeline Summarization (Open-TLS) seeks to construct coherent evolutionary narratives for a topic by distilling information from large, unstructured web streams. Existing retrieve-and-generate pipelines often fail to capture logically necessary but semantically distant evidence, resulting in macro-level causal gaps (missing intermediate stages) and micro-level element gaps (underspecified milestones). We propose Active Timeline Completion (ATC), an iterative framework that converts implicit narrative gaps into targeted retrieval questions, enabling reflection-driven evidence acquisition. ATC leverages three complementary mechanisms—parametric causal gap detection, historical trajectory analogy, and dynamic ontology-guided completion—while a Logical Utility Verification module ensures only evidence directly addressing these gaps updates the timeline. Experiments on Open-TLS demonstrate that ATC consistently outperforms strong baselines in both content coverage and temporal alignment, showing that active, question-driven retrieval improves the completeness and coherence of open-domain timelines.
%U https://aclanthology.org/2026.emnlp-main.256/
%P 5809-5826
Markdown (Informal)
[From Gaps to Active Retrieval : Open-Domain Iterative Timeline Completion via Historical Trajectory Analogy and Dynamic Ontology](https://aclanthology.org/2026.emnlp-main.256/) (Zheng et al., EMNLP 2026)
ACL