ScholarSum: Student-Teacher Abstractive Summarization via Knowledge Graph Reasoning and Reflective Refinement

2026-06-17 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

ScholarSum, a hierarchical reflective graph-based framework, addresses the challenge of balancing linguistic fluency and factual faithfulness in scientific abstractive summarization. Existing methods, including extractive and large language model (LLM)-based generative approaches, often fail to reconcile these requirements. ScholarSum emulates a student-teacher writing process, first organizing documents into a hierarchical knowledge graph to capture global logic and macro-level themes. A "student" then generates an initial draft guided by this structure, which is refined through fine-grained evidence retrieval. A "teacher-like reviewer" iteratively examines the draft for unsupported content, prompting targeted re-retrieval and rewriting. Extensive experiments demonstrate ScholarSum significantly outperforms previous baselines in both completeness and faithfulness. The code is available at https://github.com/Xiaoyu-Tao/ScholarSum.

Key takeaway

For NLP Engineers developing scientific summarization systems, ScholarSum offers a robust framework to overcome the trade-off between linguistic fluency and factual faithfulness. You should consider integrating hierarchical knowledge graphs and iterative refinement loops into your generative models. This approach can significantly enhance summary completeness and factual consistency, moving beyond limitations of current LLM-based methods and improving the reliability of automated scientific literature analysis.

Key insights

ScholarSum uses a student-teacher, graph-based approach to achieve fluent and faithful scientific abstractive summarization.

Principles

• Hierarchical knowledge graphs capture global document logic.
• Iterative refinement improves factual consistency.
• Emulating student-teacher process enhances summarization.

Method

ScholarSum segments documents into a hierarchical knowledge graph, guides initial draft generation, then iteratively refines it via a "teacher" reviewer for factual consistency and quality.

In practice

• Apply hierarchical knowledge graphs for document structuring.
• Implement iterative review for factual verification.
• Use student-teacher models for complex generation tasks.

Topics

• Abstractive Summarization
• Knowledge Graphs
• Scientific Literature
• Student-Teacher Models
• Factual Consistency
• Natural Language Processing

Code references

• Xiaoyu-Tao/ScholarSum

Best for: Research Scientist, AI Scientist, NLP Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.