A practical framework that integrates prompt compression and topic modeling is proposed that overcomes context window limitations by compressing inputs while explicitly supplementing them with extracted topic words, thereby preserving global contextual information.
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.
M. Qiao· Poster Volume 0007 The 2026...· 0 citations
Text summarization is deceptively difficult. While condensing information seems straightforward, real-world enterprise summarization of support tickets, legal documents, incident reports, and more, demands strict adherence to domain-specific guidelines, output formats, and organizational conventions. Crafting prompts that reliably satisfy these constraints is labor-intensive, requiring significant human expertise and continuous maintenance as requirements evolve. Existing automated prompt optimization methods reduce this burden through Large Language Model (LLM) critique-driven refinement, yet remain limited by static prompts that cannot adapt to the diversity of summary applications. We propose Multi-LLM Iterative Data-Adaptive Summarization (MIDAS), a multi-LLM framework that extends this paradigm with data-driven pattern learning and use-case-specific personalization, enabling automatic adaptation to different summarization requirements without manual prompt engineering. Applied to enterprise customer ticket summarization across five output formats, MIDAS achieves the strongest overall performance against state-of-the-art critique-driven optimization frameworks such as CriSPO and ZERA, improving ROUGE-1 by up to 11.0%, ROUGE-2 by up to 18.2%, and ROUGE-L by up to 8.0%, while consistently improving BERTScore F1 across all formats and output types. We additionally demonstrate cross-model and cross-domain generalization through multi-LLM configurations and finance-domain summarization benchmarks.
Karen Lee, D. Balaram, Seojun Shon et al.· 0 citations
The proposed framework using Bidirectional Long Short-Term Memory with a hypergraph and a dominating set mechanism proves to be an efficient approach to automatic summarization and has the potential to be applied in journalism, healthcare, legal analysis, and digital content management.
Pradeepa Sampath, S. Subashini, V. Shanmuganathan et al.· Artificial Intelligence and...· 0 citations
This survey presents a systematic review of 121 references spanning 2002 to 2026, tracing the evolution of TextRank-based approaches into hybrid LLM pipelines and advancing three qualified arguments.
Ahmed J. Jabur, Asmaa Abdul Azeez Dakhil, Israa Saad Mohammed et al.· Iraqi Journal for Computers...· 0 citations
This study proposes a novel two-level diagnostic protocol for benchmarking LLM-summarizers based on the stability of the generated summaries and motivates further research towards development of robust, reliable and trustworthy LLM-summarizers.
This approach combines language-adaptive mixture-of-experts embeddings with graph neural networks that model discourse structure, addressing linguistic challenges across typologically diverse low-resource languages.
Xuan-Hung Le, Thi Toan Do, Hoang-Quynh Le· Annual International ACM SIG...· 0 citations
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