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.

翻译：科学综述不仅需要总结大量文献，还需将其组织成清晰连贯的概念结构。现有自动综述生成方法通常侧重于线性文本生成，难以显式建模研究主题间的层次关系与结构化方法比较，导致在结构组织上与专家撰写的综述存在差距。我们提出MVSS，一个多视角结构化综述生成框架，能够联合生成并对齐基于引用的层次树、结构化比较表格及综述文本。MVSS遵循结构优先范式：首先构建研究领域的概念树，随后生成受树结构约束的比较表格，最终将二者共同作为文本生成的结构约束。这实现了跨结构、比较与叙述的互补多视角表征。我们引入了一个评估框架，用于衡量结构质量、比较完整性与引用保真度。在76个计算机科学主题上的实验表明，MVSS在组织性与证据支撑方面优于现有方法，达到了与专家综述相当的性能水平。