DEA-TLS: A Divide-Extract-and-Aggregate Framework for Costless and Accurate Timeline Summarization

Abstract

Timeline summarization aims to identify key events from multi-source texts and organize them into a coherent timeline of event evolution in chronological order. Existing methods generally adopt a two-stage document-level framework, which first extracts timeline information from individual documents and then aggregates the extracted results into a final timeline summary. However, such methods often suffer from high computational cost in practical applications. On the one hand, closed-source models incur substantial inference expenses; on the other hand, locally deployed open-source models still require considerable computational resources when processing long documents. To address this issue, we propose DEA-TLS, a Divide-Extract-and-Aggregate framework for Timeline Summarization, which transforms conventional document-level processing into finer-grained paragraph-level processing, thereby reducing the overall computational burden. Nevertheless, this framework also brings new challenges, including the introduction of irrelevant information, inaccurate event extraction, and redundancy during aggregation. To tackle these issues, we further design three modules: Progressive Divider, Reflective Extractor, and Hierarchical Aggregator, which are responsible for selecting high-value paragraphs, improving extraction accuracy, and reducing semantic redundancy, respectively. Experiments on the Open-TLS dataset demonstrate that our method outperforms existing baselines across multiple key metrics, providing an effective solution for achieving low-cost and high-accuracy timeline summarization.