MVSS: A Unified Framework for Multi-View Structured Survey Generation

2026-01-14

AI Summary

This work addresses the limitations of existing automatic survey generation methods, which struggle to explicitly model the hierarchical structure of research topics and methodological comparisons, often resulting in disorganized outputs lacking the structural coherence of expert-written reviews. To overcome this, the authors propose the MVSS framework, which unifies hierarchical concept trees, structured comparison tables, and narrative text under a “structure-first” paradigm. Specifically, concept trees are constructed from citations, used to constrain the generation of comparison tables, and jointly serve as structural priors to guide coherent text generation, enabling multi-view alignment and synergy. Evaluated on 76 computer science topics, the approach significantly outperforms existing methods in organizational structure and evidential support, approaching the quality of human expert surveys. The study also introduces a novel evaluation framework targeting structural quality, completeness of comparisons, and citation fidelity.

Technology Category

• Natural Language Processing: Generation
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Web Mining and Content Analysis: Bridging structured and unstructured data
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

Scientific surveys require not only summarizing large bodies of literature, but also organizing them into clear and coherent conceptual structures. Existing automatic survey generation methods typically focus on linear text generation and struggle to explicitly model hierarchical relations among research topics and structured methodological comparisons, resulting in gaps in structural organization compared to expert-written surveys. We propose MVSS, a multi-view structured survey generation framework that jointly generates and aligns citation-grounded hierarchical trees, structured comparison tables, and survey text. MVSS follows a structure-first paradigm: it first constructs a conceptual tree of the research domain, then generates comparison tables constrained by the tree, and finally uses both as structural constraints for text generation. This enables complementary multi-view representations across structure, comparison, and narrative. We introduce an evaluation framework assessing structural quality, comparative completeness, and citation fidelity. Experiments on 76 computer science topics show MVSS outperforms existing methods in organization and evidence grounding, achieving performance comparable to expert surveys.

Problem

• survey generation
• hierarchical relations
• structured comparison
• multi-view representation
• scientific literature

Innovation

• multi-view
• structured survey generation
• hierarchical tree
• comparison table
• structure-first paradigm