Recent retrieval-augmented models enhance basic methods by building a hierarchical structure over retrieved text chunks through recursive embedding, clustering, and summarization. The most relevant information is then retrieved from both the original text and generated summaries. However, such approaches face limitations with dynamic datasets, where adding or removing documents over time complicates the updating of hierarchical representations formed through clustering. We propose a new algorithm to efficiently maintain the recursive-abstractive tree structure in dynamic datasets, without compromising performance. Additionally, we introduce a novel post-retrieval method that applies query-focused recursive abstractive processing to substantially improve context quality. Our method overcomes the limitations of other approaches by functioning as a black-box post-retrieval layer compatible with any retrieval algorithm. Both algorithms are validated through extensive experiments on real-world datasets, demonstrating their effectiveness in handling dynamic data and improving retrieval performance.
RetrievalSum: A Retrieval Enhanced Framework for Abstractive Summarization
Shanghai Key Laboratory of Intelligent Information Processing, Fudan University（复旦大学上海市智能信息处理重点实验室）；School of Computer Science, Fudan University（复旦大学计算机科学技术学院）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Neural Sequence-to-Sequence Modeling with Attention by Leveraging Deep Learning Architectures for Enhanced Contextual Understanding in Abstractive Text Summarization
Systematic Exploration of Dialogue Summarization Approaches for Reproducibility, Comparative Assessment, and Methodological Innovations for Advancing Natural Language Processing in Abstractive Summarization
SRM Institute of Science and Technology（SRM科学技术学院）；SRM University, AP（SRM大学（安得拉邦））
MedRLM: Recursive Multimodal Health Intelligence for Long-Context Clinical Reasoning, Sensor-Guided Screening, Evidence-Grounded Decision Support, and Community-to-Tertiary Referral Optimization
School of Information, Computer；Communication Technology Sirindhorn International Institute of Technology, Thammasat University Pathum Thani, Thailand 1
RELOOP: Recursive Retrieval with Multi-Hop Reasoner and Planners for Heterogeneous QA
University of New South Wales（新南威尔士大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Mohamed Bin Zayed University of Artificial Intelligence（莫扎伊德大学人工智能学院）；Technology Innovation Institute（技术创新研究所）
Evaluation of Chunking Strategies for Effective Text Embedding in Low-Resource Language on Agricultural Documents
Department of Big Data, Chungbuk National University, Cheongju-si, South Korea（大数据系， Chungbuk国立大学，韩国Cheongju市）；Department of Computer Science, Chungbuk National University, Cheongju-si, South Korea（计算机科学系， Chungbuk国立大学，韩国Cheongju市）；BigDataLabs Co., Ltd. Department of Management Information Systems, Chungbuk National University, South Korea（BigDataLabs公司 管理信息系， Chungbuk国立大学，韩国）
