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.
The proposed DEA-TLS is 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.
Ming Qiao· Poster Volume 0007 The 2026...· 0 citations
This work proposes 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.
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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