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

• URL: http://arxiv.org/abs/2601.09504v1
• Date: Wed, 14 Jan 2026 14:11:39 GMT
• Title: MVSS: 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,
• Abstract summary: We propose MVSS, a multi-view structured survey generation framework.<n>It generates and aligns citation-grounded hierarchical trees, structured comparison tables, and survey text.<n>Experiments on 76 computer science topics show MVSS outperforms existing methods in organization and evidence grounding.
• Score: 22.149843949044712
• 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.

Related papers

• Structure-R1: Dynamically Leveraging Structural Knowledge in LLM Reasoning through Reinforcement Learning [29.722512436773638]
  We propose textscStructure-R1, a framework that transforms retrieved content into structured representations optimized for reasoning.<n>We show that textscStructure-R1 consistently achieves competitive performance with a 7B-scale backbone model.<n>Our theoretical analysis demonstrates how structured representations enhance reasoning by improving information density and contextual clarity.
  arXiv  Detail & Related papers  (2025-10-16T23:19:28Z)
• Struc-EMB: The Potential of Structure-Aware Encoding in Language Embeddings [16.728984584960738]
  This paper introduces and systematically evaluates a new paradigm for generating structure-aware text embeddings.<n>We investigate two primary in-process methods: sequential concatenation and parallel caching.<n>Our analysis reveals critical trade-offs: sequential concatenation excels with noisy, moderate-length contexts, while parallel caching scales more effectively to long, high-signal contexts but is more susceptible to distractors.
  arXiv  Detail & Related papers  (2025-10-09T19:45:54Z)
• SurveyG: A Multi-Agent LLM Framework with Hierarchical Citation Graph for Automated Survey Generation [4.512335376984058]
  Large language models (LLMs) are increasingly adopted for automating survey paper generation.<n>We propose textbfSurveyG, an LLM-based agent framework that integrates textithierarchical citation graph<n>The graph is organized into three layers: textbfFoundation, textbfDevelopment, and textbfFrontier, to capture the evolution of research from seminal works to incremental advances and emerging directions.
  arXiv  Detail & Related papers  (2025-10-09T03:14:20Z)
• Context-Aware Hierarchical Taxonomy Generation for Scientific Papers via LLM-Guided Multi-Aspect Clustering [59.54662810933882]
  Existing taxonomy construction methods, leveraging unsupervised clustering or direct prompting of large language models, often lack coherence and granularity.<n>We propose a novel context-aware hierarchical taxonomy generation framework that integrates LLM-guided multi-aspect encoding with dynamic clustering.
  arXiv  Detail & Related papers  (2025-09-23T15:12:58Z)
• Learning Discrete Bayesian Networks with Hierarchical Dirichlet Shrinkage [52.914168158222765]
  We detail a comprehensive Bayesian framework for learning DBNs.<n>We give a novel Markov chain Monte Carlo (MCMC) algorithm utilizing parallel Langevin proposals to generate exact posterior samples.<n>We apply our methodology to uncover prognostic network structure from primary breast cancer samples.
  arXiv  Detail & Related papers  (2025-09-16T17:24:35Z)
• Beyond Chunking: Discourse-Aware Hierarchical Retrieval for Long Document Question Answering [51.7493726399073]
  We present a discourse-aware hierarchical framework to enhance long document question answering.<n>The framework involves three key innovations: specialized discourse parsing for lengthy documents, LLM-based enhancement of discourse relation nodes, and structure-guided hierarchical retrieval.
  arXiv  Detail & Related papers  (2025-05-26T14:45:12Z)
• Mixture of Structural-and-Textual Retrieval over Text-rich Graph Knowledge Bases [78.62158923194153]
  Text-rich Graph Knowledge Bases (TG-KBs) have become increasingly crucial for answering queries by providing textual and structural knowledge.<n>We propose a Mixture of Structural-and-Textual Retrieval (MoR) to retrieve these two types of knowledge via a Planning-Reasoning-Organizing framework.
  arXiv  Detail & Related papers  (2025-02-27T17:42:52Z)
• Think&Cite: Improving Attributed Text Generation with Self-Guided Tree Search and Progress Reward Modeling [63.98194996746229]
  Large language models (LLMs) are prone to hallucination and producing factually incorrect information.<n>We propose a novel framework, called Think&Cite, and formulate attributed text generation as a multi-step reasoning problem integrated with search.
  arXiv  Detail & Related papers  (2024-12-19T13:55:48Z)
• Sequential Visual and Semantic Consistency for Semi-supervised Text Recognition [56.968108142307976]
  Scene text recognition (STR) is a challenging task that requires large-scale annotated data for training. Most existing STR methods resort to synthetic data, which may introduce domain discrepancy and degrade the performance of STR models. This paper proposes a novel semi-supervised learning method for STR that incorporates word-level consistency regularization from both visual and semantic aspects.
  arXiv  Detail & Related papers  (2024-02-24T13:00:54Z)
• Structured Multi-modal Feature Embedding and Alignment for Image-Sentence Retrieval [12.050958976545914]
  The current state-of-the-art image-sentence retrieval methods implicitly align the visual-textual fragments. We propose a novel Structured Multi-modal Feature Embedding and Alignment model for image-sentence retrieval. In particular, the relations of the visual and textual fragments are modeled by constructing Visual Context-aware Structured Tree encoder (VCS-Tree) and Textual Context-aware Structured Tree encoder (TCS-Tree) with shared labels.
  arXiv  Detail & Related papers  (2021-08-05T07:24:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.