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

2026-06-18 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

ScholarSum is a hierarchical reflective graph-based framework designed for abstractive summarization of scientific literature, aiming to reconcile linguistic fluency with factual faithfulness. It emulates a student-teacher writing process, first organizing a document into a hierarchical knowledge graph with semantically coherent units and multi-layered community structures to capture global logic. A "student" then generates an initial draft guided by this structure, which is subsequently refined through fine-grained evidence retrieval. A "teacher-like reviewer" iteratively examines the draft, identifies unsupported content, and prompts targeted re-retrieval and rewriting until quality standards are met. Experiments on ArXiv and PubMed datasets demonstrate ScholarSum significantly outperforms baselines like T5, LED, PEGASUS, SumCoT, QA-prompting (with DeepSeek and Qwen), and Naive RAG in completeness and faithfulness, as measured by ROUGE, METEOR, BERTScore, and MiniCheck. The framework shows robustness with a decoding temperature of 0.8.

Key takeaway

For AI Scientists and Machine Learning Engineers developing scientific summarization systems, ScholarSum offers a robust approach to enhance both fluency and factual accuracy. You should consider integrating hierarchical knowledge graph construction for macro-level planning and an iterative student-teacher refinement loop for fine-grained factual verification. This method significantly reduces hallucinations and improves content coverage, especially for complex technical documents like medical literature. Implement explicit feedback mechanisms to calibrate domain-specific quality.

Key insights

ScholarSum integrates hierarchical knowledge graphs and iterative student-teacher refinement for fluent, factually faithful scientific summarization.

Principles

• Hierarchical graph structures guide global coherence.
• Iterative refinement corrects factual inconsistencies.
• External references calibrate domain-specific evaluation.

Method

ScholarSum constructs a hierarchical knowledge graph, then a "student" drafts summaries. A "teacher" iteratively evaluates, retrieves evidence, and prompts revisions until quality thresholds are met.

In practice

• Use instruction-tuned LLMs for entity/relation extraction.
• Employ community detection for thematic planning.
• Ground revisions in explicit evidence for accuracy.

Topics

• Abstractive Summarization
• Knowledge Graphs
• Student-Teacher Learning
• Factual Consistency
• Large Language Models
• Scientific Literature

Code references

• Xiaoyu-Tao/ScholarSum

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.