StructSurvey: Structured Agentic Retrieval for Automated Survey Paper Generation

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

STRUCTSURVEY is a hierarchical multiagent framework designed to automate survey paper generation by addressing the challenge of synthesizing rapidly growing scientific publications. Unlike existing Large Language Model (LLM) methods that process unstructured data, STRUCTSURVEY shifts structural reasoning from generation to retrieval. It achieves this by dynamically constructing graph-based representations of entities, relations, and topical taxonomies. Evaluated on a new reference-grounded benchmark of ACL survey papers, STRUCTSURVEY improved ROUGE-1 recall by +2.9 and ROUGE-2 recall by +1.0 on average compared to embedding-only retrieval baselines, without reducing precision. Furthermore, it enhanced LLM-as-a-Judge ratings for logical structure, depth, and synthesis, demonstrating that explicit structural retrieval produces surveys more aligned with human organization and reasoning.

Key takeaway

For Research Scientists or NLP Engineers developing automated summarization tools, STRUCTSURVEY demonstrates a critical shift: pre-structuring retrieved information significantly enhances output quality. You should consider integrating explicit structural reasoning, perhaps via graph-based knowledge representations, into your retrieval pipelines. This approach can yield more logically organized and deeply synthesized long-form scientific summaries, moving closer to human-quality output and improving overall utility.

Key insights

STRUCTSURVEY uses graph-based structural retrieval to improve automated survey generation by LLMs.

Principles

• Shift structural reasoning from generation to retrieval.
• Graph-based representations enhance LLM synthesis.

Method

STRUCTSURVEY employs a hierarchical multiagent framework to dynamically build graph-based representations of entities, relations, and topical taxonomies for structured data retrieval.

In practice

• Automate scientific survey generation.
• Improve LLM-generated document structure.

Topics

• Automated Survey Generation
• Large Language Models
• Multiagent Systems
• Graph-based Knowledge Representation
• Information Retrieval
• Scientific Summarization
• ROUGE Score

Best for: AI Scientist, NLP Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.