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.
Abstract (translated)
科学研究的综述不仅需要总结大量的文献，还需要将这些文献组织成清晰连贯的概念结构。现有的自动综述生成方法通常集中在线性文本生成上，并且难以明确地建模研究主题之间的层次关系和有结构的方法比较，这导致与专家撰写的综述相比，在结构组织方面存在差距。 为此，我们提出了一种名为MVSS的多视角结构化综述生成框架。该框架旨在同时生成并对齐基于引用的层级树、结构化的对比表格以及综述文本。MVSS遵循一种“以结构为先”的方法：首先构建研究领域的概念树，然后在此基础上生成受树约束的比较表，并最终将这两者作为结构限制来辅助文本生成。这种做法可以实现跨越结构、对比和叙述的互补多视角表示。 我们还引入了一种评估框架，用于评价综述在结构性质量、对比完整性和引用忠实度方面的表现。实验结果显示，在针对76个计算机科学主题进行测试时，MVSS相较于现有方法在组织和证据支撑方面表现出色，并且其整体性能可以与专家撰写的综述相媲美。
URL
https://arxiv.org/abs/2601.09504
