ScholarSum: Student-Teacher Abstractive Summarization via Knowledge Graph Reasoning and Reflective Refinement
arXiv:2606.18850v1 Announce Type: new Abstract: Abstractive summarization plays a crucial role in enabling efficient understanding of scientific literature, yet it inherently demands both linguistic fluency and factual faithfulness. Existing approaches often fail to reconcile these two requirements. Extractive methods rely on rigid sentence splicing that disrupts macro-level logical coherence, while large language model (LLM)-based generative approaches, despite mastering linguistic fluency, exhibit limited factual consistency. In this work, we propose ScholarSum, a hierarchical reflective gra
The proliferation of scientific literature and the limitations of current summarization methods (both extractive and LLM-based) are driving innovation in more accurate and coherent methods.
Improving the accuracy and factual faithfulness of abstractive summarization directly impacts the efficiency of knowledge acquisition and research, particularly in rapidly evolving technical fields.
The proposed ScholarSum system, by integrating knowledge graph reasoning and reflective refinement, offers a potential advancement in overcoming the trade-off between linguistic fluency and factual consistency in AI-driven content generation.
- · AI researchers
- · Scientific publishers
- · Knowledge management platforms
- · Inefficient manual summarization processes
- · LLMs without strong factual grounding
Enhanced ability to rapidly synthesize and understand complex scientific information becomes more widespread.
This could accelerate research cycles and improve the pace of scientific discovery across various domains.
More reliable AI-generated summaries could lead to new forms of scientific collaboration and data-driven insights.
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Read at arXiv cs.CL