MVSS: A Unified Framework for Multi-View Structured Survey Generation
Yinqi Liu, Yueqi Zhu, Yongkang Zhang, Feiran Liu, Yutong Shen, Yufei Sun, Xin Wang, Renzhao Liang, Yidong Wang, and Cunxiang Wang

TL;DR
MVSS is a comprehensive framework for automatic survey generation that models hierarchical structures, comparison tables, and narrative text simultaneously, improving structural organization and evidence grounding.
Contribution
It introduces a structure-first, multi-view approach that jointly generates and aligns hierarchical trees, comparison tables, and survey text, advancing automatic survey creation.
Findings
MVSS outperforms existing methods in survey organization and evidence grounding.
Achieves results comparable to expert surveys across multiple metrics.
Demonstrates effectiveness on 76 computer science topics.
Abstract
Scientific surveys require not only summarizing large bodies of literature, but also organizing them into clear and coherent conceptual structures. However, 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 substantial gaps in structural organization and evidence presentation compared to expert-written surveys. To address this limitation, 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 tree that captures the conceptual organization of a research domain, then generates comparison tables constrained by the tree structure,…
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