dorsal/arxiv
View SchemaMVSS: A Unified Framework for Multi-View Structured Survey Generation
| Authors | Yinqi Liu, Yueqi Zhu, Yongkang Zhang, Xinfeng Li, Feiran Liu, Yufei Sun, Xin Wang, Renzhao Liang, Yidong Wang, Cunxiang Wang |
|---|---|
| Categories | |
| ArXiv ID | 2601.09504vv1 |
| URL | https://arxiv.org/abs/2601.09504 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
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.
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"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.",
"arxiv_id": "2601.09504",
"authors": [
"Yinqi Liu",
"Yueqi Zhu",
"Yongkang Zhang",
"Xinfeng Li",
"Feiran Liu",
"Yufei Sun",
"Xin Wang",
"Renzhao Liang",
"Yidong Wang",
"Cunxiang Wang"
],
"categories": [
"cs.CL"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "MVSS: A Unified Framework for Multi-View Structured Survey Generation",
"url": "https://arxiv.org/abs/2601.09504",
"version": "v1"
},
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"type": "Model",
"variant": "snapshot-2026-01-17",
"version": "0.1.0"
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