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        <title>From Moments to Milestones: Incremental Timeline Summarization Leveraging Large Language Models</title>
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    <abstract>Timeline summarization (TLS) is essential for distilling coherent narratives from a vast collection of texts, tracing the progression of events and topics over time. Prior research typically focuses on either event or topic timeline summarization, neglecting the potential synergy of these two forms. In this study, we bridge this gap by introducing a novel approach that leverages large language models (LLMs) for generating both event and topic timelines. Our approach diverges from conventional TLS by prioritizing event detection, leveraging LLMs as pseudo-oracles for incremental event clustering and the construction of timelines from a text stream. As a result, it produces a more interpretable pipeline. Empirical evaluation across four TLS benchmarks reveals that our approach outperforms the best prior published approaches, highlighting the potential of LLMs in timeline summarization for real-world applications.</abstract>
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%0 Conference Proceedings
%T From Moments to Milestones: Incremental Timeline Summarization Leveraging Large Language Models
%A Hu, Qisheng
%A Moon, Geonsik
%A Ng, Hwee Tou
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F hu-etal-2024-moments
%X Timeline summarization (TLS) is essential for distilling coherent narratives from a vast collection of texts, tracing the progression of events and topics over time. Prior research typically focuses on either event or topic timeline summarization, neglecting the potential synergy of these two forms. In this study, we bridge this gap by introducing a novel approach that leverages large language models (LLMs) for generating both event and topic timelines. Our approach diverges from conventional TLS by prioritizing event detection, leveraging LLMs as pseudo-oracles for incremental event clustering and the construction of timelines from a text stream. As a result, it produces a more interpretable pipeline. Empirical evaluation across four TLS benchmarks reveals that our approach outperforms the best prior published approaches, highlighting the potential of LLMs in timeline summarization for real-world applications.
%U https://aclanthology.org/2024.acl-long.390
%P 7232-7246

Markdown (Informal)

[From Moments to Milestones: Incremental Timeline Summarization Leveraging Large Language Models](https://aclanthology.org/2024.acl-long.390) (Hu et al., ACL 2024)

• From Moments to Milestones: Incremental Timeline Summarization Leveraging Large Language Models (Hu et al., ACL 2024)

ACL

• Qisheng Hu, Geonsik Moon, and Hwee Tou Ng. 2024. From Moments to Milestones: Incremental Timeline Summarization Leveraging Large Language Models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7232–7246, Bangkok, Thailand. Association for Computational Linguistics.