An Explainable Hybrid Deep Learning Framework for Outcome-Oriented Long-Document Summarization Using Adaptive Feature Learning and Decision Optimization
This paper proposes an explainable hybrid deep learning framework that combines dense retrieval, parameter-efficient LED fine-tuning with LoRA, and outcome-oriented optimization to generate instructional summaries aligned with learning objectives, achieving state-of-the-art performance on educational datasets like QMSum, GovReport, and NILD.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
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