A method for generating a multi-view structured review

Abstract

The application relates to the technical field of natural language processing, and specifically discloses a multi-view structured review generation method, which redefines the review generation as a cross-view structure learning problem for the first time, jointly optimizes three kinds of mutually enhanced representations, captures a hierarchical knowledge tree HKT of field concept organization, discloses a tree-induced comparison table of a contrastive discrimination axis, and generates a text review aligned with two structures. The method comprises the following steps: evidence retrieval and multi-modal structuring, construction of a hierarchical knowledge tree and optimization, generation of a structured comparison table, generation of a coherent text review based on cross-format alignment, and the like. The multi-view structured review generation method disclosed by the application applies constraints between different views through cross-format alignment, realizes closed-loop optimization beyond the pipeline method, and has improvements in structural clarity, comparison integrity and reference fidelity compared with a powerful baseline, and achieves a performance close to that of an expert in concept organization and methodology comparison.

Description

Technical Field

[0001] This invention relates to the field of natural language processing technology, and in particular to a method for generating multi-view structured overviews. Background Technology

[0002] In several cutting-edge areas of natural language processing, methodological innovations progress far faster than human-written reviews can keep pace. In rapidly evolving fields such as large-scale language models, retrieval-enhanced generation, and multimodal reasoning, major advancements often emerge within months, while reviews lag behind by one to two publication cycles. This gap limits researchers' ability to obtain timely, structured overviews. With the increasing quantity and heterogeneity of literature, researchers not only need to keep up with new methods but also understand how ideas branch out and how concepts interact.

[0003] Recent advances in automated review generation demonstrate that large language models (LLMs) can draft reviews with minimal human intervention. However, existing systems share a common structural weakness: LLM-based workflows can only generate linear narratives and cannot integrate diverse research trajectories; clustering-based taxonomy relies on semantics of headings alone and sparse supervision, resulting in unstable hierarchical structures and failing to assess the quality of tree structures; topic modeling methods can capture distributed topics but cannot capture conceptual relationships. Although experiments show that automatically generated tables can capture rich comparative structures, the academic community still lacks reliable metrics for evaluating table quality.

[0004] These issues collectively reveal a broader bottleneck: the lack of reliable, semantically grounded structured representations to support flexible tables, stable hierarchical structures, and evidence-consistent text synthesis. Summary of the Invention

[0005] The purpose of this invention is to provide a multi-view structured overview generation method that directly addresses key gaps in previous work, ensuring that the generated tree diagrams, tables, and text can form a coherent, navigable, and factual representation of the underlying research domain.

[0006] To achieve the above objectives, the present invention provides a method for generating multi-view structured overviews, comprising the following steps: S1. Evidence retrieval and multimodal structuring; S2. Construct and optimize a hierarchical knowledge tree; S3. Generate a structured comparison table; S4. Generate a coherent text summary based on cross-format alignment.

[0007] Preferably, S1 is as follows: Given topic query and literature corpus The enhanced encoder retrieves a collection of papers related to the topic. The expression is as follows: in, For use in executing query vectors With document corpus Semantic matching of document vectors in the document; calculation by the backend retrieval machine. and The dense vector cosine similarity or mixed rating signal of the entries is used to return the previous entries in descending order of score. A collection of relevant documents ; Collection of papers Grouped into bundles of papers: ; in, Represents the candidate set; The total number of elements in the candidate set, i.e., the number of candidates; Prompt a set of heterogeneous large language models Generate candidate hierarchical outlines for each topic, as follows: ; in, Refers to the first One model, For index variables, It is the total number of models, that is, the total number of models used. Processing or integrating different models; Indicates by the first A model The generated output results This represents a generating function or forward propagation process, with the model as input. Contextual information and candidate set The output is the generated result corresponding to this model; The evaluation model assesses each candidate level of outline based on coverage, organization, and relevance, and generates a score vector. ; in, For the scoring dimension or feature dimension, each output is mapped to a... In a 3D vector space; After calculating the total score, the outline of the standard levels is obtained through screening. The formula for calculating the total score is as follows: ; in, Indicates by the first A model The total score of the generated output results It is a loop variable, representing the first... One evaluation indicator, For the first The weight of each evaluation indicator, For the first The outline is in the first Quality scores on each indicator; The outline of the standard hierarchy is denoted as: ; in, It is the optimal review outline ultimately selected by the system, used to guide the generation of the subsequent review text; This is the maximum value operator, which means selecting the index that maximizes the score function value from all candidate sets. .

[0008] Preferably, S2 includes the following steps: S21. Perform node-level evidence retrieval: For each text descriptor in the specification hierarchy outline tree nodes Perform node-level evidence retrieval: ; in, To refine the number of iterations, For the first A set of supporting materials retrieved in the next iteration. Used to control exploration-refinement progress: ; when At that time, use the parameters from the exploration phase. ;when At that time, use the parameters of the refining stage. ; Maintain a reference set with diversity and stability constraints. ; in: ; Upper and lower constraints on the number of nodes; S22. Generate parallel nodes and assemble the initial hierarchical knowledge tree: Given a descriptor and Generate node summary : ; Assemble into a complete tree representation: ; Define the hierarchy of soft structure consistency penalty constraints: ; in, Tree node The parent node, This is used to penalize situations where the range of a child node exceeds that of its parent node. Used to punish situations where sibling nodes overlap excessively.

[0009] Preferably, S2 further includes: S23. Simultaneously optimize structural quality and reference quality indicators: set up Let represent the evaluation scores of a tree in the dimensions of coverage, organization, and relevance of a large language model (LLM). , These represent the reference precision and recall of all node-reference pairs in Natural Language Inference (NLI), respectively. Define the comprehensive scoring function This is achieved by providing feedback to the model for low-scoring dimensions, thereby defining and updating the model. : ; in, These are preset weight hyperparameters; The loop terminates when the following iteration conditions are met, yielding a refined hierarchical knowledge tree: ; in, , The threshold value is set.

[0010] Preferably, S3 is as follows: Predefine a set of table schemas For each tree node Using the pattern selection model The table schema corresponding to each node of the refined hierarchical knowledge tree Rate it: ; Before screening model ; For tree nodes Selection mode at the location Definition Table ,surface row correspondence method Column corresponding attributes ,surface Each cell Generated under a constrained decoding mechanism that only allows the retention of... Supported content : ; Factuality of the Fact Score Assessment Table through Natural Language Inference (NLI): ; in, It is a set of non-empty cells; correct low-scoring cells. This represents the atomic statements derived from the cell text content of a structured comparison table. Indicates starting from the current tree node Related collection of papers The references selected in the text are evidence.

[0011] Preferably, S4 is as follows: Text review Previous chapters and selected tree nodes Related, for each node Chapters are generated by using node summaries, local tables, and citations as conditions: ; The entire review is based on The indicated assembly sequence is as follows: .

[0012] Preferably, S4 also includes a cross-format alignment target: Define cross-format alignment score: ; in, Used to determine whether a table row or column is anchored to the corresponding tree structure node. This concept is used to measure whether the content of a chapter follows a tree-like structure. Used to measure whether the statements in the text are consistent with the table entries; Hierarchical knowledge tree based on cross-format alignment score iterative correction K Comparison Table T and text review Until cross-format alignment scores Stable output, multi-view structured overview, cross-format aligned scores The expression is as follows: ; in, 、 These are the weighting coefficients.

[0013] Therefore, the present invention employs the above-mentioned multi-view structured overview generation method, and the beneficial effects are as follows: (1) This invention achieves closed-loop optimization by jointly optimizing hierarchical knowledge trees, tree-induced comparison tables and aligned text summaries; in tests on 50 computer science topics, the structural clarity is improved by 18.3%, the comparison completeness is improved by 24.6%, and the citation fidelity is improved by 11.2%. The conceptual organization and methodological comparison performance are close to the expert level, which significantly breaks through the limitations of traditional pipeline methods and provides a reliable framework for automated literature synthesis.

[0014] (2) This invention solves the problem of lack of structure in existing automatic review systems. The generated multi-view structure can clearly reveal the evolution of domain concept branches and the comparative dimensions of methodologies. Compared with linear text reviews, it greatly improves the navigation and analytical capabilities of reviews, helps researchers quickly grasp the domain knowledge architecture, core method differences and development trajectory, effectively makes up for the shortcomings of manual reviews, and adapts to the needs of rapidly developing scientific research fields.

[0015] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0016] Figure 1 This is an example of an existing linear text review generation process in an embodiment of a multi-view structured review generation method of the present invention; Figure 2 This invention provides an embodiment of a multi-view structured overview generation method, which outputs a hierarchical knowledge tree, a comparison table, and an evidence-based text synthesis flowchart. Figure 3 This is a flowchart illustrating a specific implementation of an embodiment of the multi-view structured overview generation method of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0018] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects.

[0019] One noteworthy structure in this invention is not a tree-like outline, but rather a potential conceptual hierarchy whose nodes must satisfy three orthogonal constraints: (1) semantic coherence, (2) evidence-based justification, and (3) cross-perspective compatibility with table schemas and text composition, such as... Figure 2 As shown, existing systems fail to jointly optimize these constraints, making consistent multi-perspective representations fundamentally impossible. To address this bottleneck, this invention introduces Hierarchical Knowledge Tree (HKT), a framework for representing the organization of concepts within a research domain. Building on advancements in hierarchical topic modeling and topic hierarchy visualization, HKT provides a principled approach to capturing the branching evolution of ideas, the development of subdomains, and the relationships between methodological paradigms. This fills a long-standing gap: despite recent progress in fidelity based on Natural Language Inference (NLI) and evidence-based evaluation, domain taxonomy remains largely heuristic and lacks systematic validation.

[0020] Based on HKT, this invention introduces a Multi-View Structured Review (MVSS) framework, which treats review generation as a joint structure learning problem, rather than... Figure 1 The linear text generation process is illustrated. Multi-view Structured Review (MVSS) generates three mutually constraining and reinforcing representations: (1) a hierarchical tree defining the domain conceptual architecture, (2) a structured comparison table whose patterns are derived from and aligned with the domain conceptual architecture, and (3) a coherent text review explicitly conditional on these two structures. MVSS does not generate these views independently, but rather enforces cross-view constraints and a shared evidence base. This closed-loop, structure-first design constitutes a paradigm distinct from existing review generation based on Large Language Models (LLMs), complementing and expanding upon the latest efforts to re-examine scientific abstracts in the era of LLMs.

[0021] Technically, the Multi-View Structured Review (MVSS) of this invention is not merely a combination of multiple components; rather, it restructures the review generation problem as a cross-view structure learning problem. Most importantly, the cross-format alignment objective ensures mutual constraints between trees, tables, and text—a capability lacking in previous abstracting or classification pipelines. Experiments on 50 computer science topics demonstrate that, compared to state-of-the-art systems, the MVSS of this invention significantly improves structural clarity, relative completeness, and citation fidelity, approaching the quality of expert-written reviews.

[0022] This invention proposes a hierarchical knowledge tree (HKT) and, for the first time, establishes a systematic protocol for evaluating structural clarity, topic coverage, and reference-based fidelity.

[0023] This invention introduces MVSS, the first multi-view overview generation framework capable of generating aligned hierarchical trees, adaptive comparison tables, and evidence-based text. MVSS, with structural alignment as its core objective, overcomes the problems of fragmented topic organization and inconsistent conceptual relationships.

[0024] Experiments on 50 topics showed that, compared to systems such as AutoSurvey, MVSS improved structural clarity by 2%, table completeness by 31%, and citation consistency by 2%. Human evaluation further rated MVSS's performance in conceptual organization and methodological comparison as near-expert level.

[0025] With the accelerating pace of scientific publishing, the automatic generation of reviews has garnered increasing attention. Recent research has explored assisted or automated literature reviews, as well as the neural systems underlying review-style overviews. Most existing methods treat this task as a variant of multi-document summarization, generating a single linear narrative by retrieving or selecting papers. Retrieval-enhanced processes improve factual evidence by presenting relevant paragraphs before generation and are often combined with powerful neural sorters or generative readers. Large Language Model (LLM)-based summarization frameworks further integrate evidence into coherent text and can be directly optimized for human preference signals. Recent benchmarks and systems highlight citation-aware generation, fact-driven training, and criticism-guided rewriting or candidate reordering to improve factual consistency and coherence.

[0026] Despite these advances, current systems fundamentally lack structured abstraction. They describe papers sequentially but fail to reveal higher-level organizational structures, such as methodological branches, conceptual hierarchies, or comparative dimensions across work domains. Consequently, these plain-text reviews remain difficult to navigate and offer limited insight into the structure or development of a particular field. In contrast, Multi-View Review Systems (MVSS) redefine review generation as a multi-view knowledge synthesis problem. MVSS not only generates text but also collectively constructs: (1) a hierarchical knowledge tree revealing how ideas branch and connect; (2) a structured comparison table supporting cross-paper and cross-paradigm analysis; and (3) evidence-based narrative text. Embedding this conceptual structure directly into the output makes reviews easier to navigate, more analytical, and more aligned with how researchers organize their domain knowledge.

[0027] Research on the representation and organization of scientific knowledge encompasses document-level embedding, graph-based retrieval, and bibliometric taxonomy. Domain-specific transformers and citation-aware encoders can capture semantic relevance, but they operate at the level of individual papers rather than conceptual units. While graph- and topic-structure-based modeling methods can connect documents, they often generate flat or poorly organized graphs, lacking the hierarchical abstraction needed to reflect the development of scientific fields. Bibliometric clustering can provide a global structure but cannot generate fine-grained, citation-based concept groupings suitable for comprehensive analysis at the review level.

[0028] Meanwhile, iterative optimization and multi-signal evaluation improve the reliability of the output of long text large language models (LLMs). Based on reflective self-correction, LLMs provide supplementary signals for evaluating and correcting generated content, serving as judge scoring, scientific fact verification, and cross-encoder reordering. However, these methods treat structure merely as a byproduct of text generation and do not establish any mechanism to ensure consistent hierarchical structures, consistent conceptual relationships, or citation-based organization across multiple review formats.

[0029] The Multi-View Review System (MVSS) integrates and extends these two approaches by treating the structure itself as an optimization objective. It employs a multi-model consensus procedure to stabilize hierarchical representations and reduce model-induced biases; a bi-objective refinement loop to jointly optimize structural consistency and citation validity; and structure-aware evaluation signals that iteratively guide the regeneration process until both hierarchical quality and evidence consistency converge. By combining hierarchical abstraction with multi-signal verification, MVSS directly addresses key gaps in previous work, ensuring that the generated tree diagrams, tables, and text form a coherent, navigable, and factually accurate representation of the underlying research domain.

[0030] The Multi-View Overview System (MVSS) of this invention transforms unstructured paper collections into three mutually aligned views about a specific domain: a hierarchical knowledge tree. K A set of structured comparison tables T And a text review S Formally, given a topic query... and corpus D The Multi-View Overview System (MVSS) learns an underlying conceptual hierarchy and uses it as the skeleton of all views: .

[0031] This invention describes the process in four stages: (1) evidence retrieval and multi-model structuring, (2) hierarchical tree construction, (3) tree-based table generation, and (4) cross-format review generation.

[0032] In this invention, Representing tree nodes, these nodes form a rooted tree. Having a parent relationship and child nodes Each node v is associated with a text descriptor. and a set of supporting papers Related. Tables use In this representation, rows represent methods, columns represent attributes (such as datasets, metrics), and in summary, S is a series of parts corresponding to subsets of tree nodes. .

[0033] like Figure 3 As shown, a method for generating multi-view structured reviews includes the following steps: S1. Evidence retrieval and multimodal structuring, specifically: During evidence retrieval, given a topic query and literature corpus This invention retrieves a collection of papers related to a specific topic using a search-enhanced encoder. The expression is as follows: ; in, For use in executing query vectors With document corpus Semantic matching of document vectors; computation via a backend retrieval system (e.g., a dual-encoder architecture). and The dense vector cosine similarity or mixed rating signal of the entries is used to return the previous entries in descending order of score. A collection of relevant documents .

[0034] This invention will collect papers Grouped into bundles of papers: ; in, Represents the candidate set; The total number of elements in the candidate set, i.e., the number of candidates.

[0035] It also suggests a set of heterogeneous large-scale language models. Generate candidate hierarchical outlines for each topic, as follows: ; in, Refers to the first One model, For index variables, It is the total number of models, that is, the total number of models used. Processing or integrating different models; Indicates by the first A model The generated output results This represents a generating function or forward propagation process, with the model as input. Contextual information and candidate set The output is the generated result corresponding to this model; each This represents a possible hierarchical decomposition of the theme.

[0036] When selecting formulas, the evaluation model assesses each candidate level outline from dimensions such as coverage, organization, and relevance, and generates a score vector: ; in, For the scoring dimension or feature dimension, each output is mapped to a... In a 3D vector space.

[0037] After calculating the total score, the outline of the standard levels is obtained through screening. The formula for calculating the total score is as follows: ; in, Indicates by the first A model The total score of the generated output results It is a loop variable, representing the first... One evaluation indicator, For the first The weight of each evaluation indicator, For the first The outline is in the first Quality scores on each indicator.

[0038] The outline of the standard hierarchy is denoted as: ; in, It is the optimal review outline ultimately selected by the system, used to guide the generation of the subsequent review text; This is the maximum value operator, which means selecting the index that maximizes the score function value from all candidate sets. .

[0039] S2. Construct and optimize a hierarchical knowledge tree, including the following steps: S21. Perform node-level evidence retrieval: For each text descriptor in the specification hierarchy outline tree nodes Perform node-level evidence retrieval: ; in, To refine the number of iterations, For the first A set of supporting materials retrieved in the next iteration. Used to control exploration-refinement progress: ; when At that time, use the parameters from the exploration phase. ;when At that time, use the parameters of the refining stage. .

[0040] This invention maintains a reference set with diversity and stability constraints. ; in: ; This sets upper and lower constraints on the number of nodes.

[0041] This decouples the early high-recall exploration from the later high-precision refinement, and also prevents citation shifts and over-reliance on a few papers.

[0042] S22. Generate parallel nodes and assemble the initial hierarchical knowledge tree: Given a descriptor and Generate node summary : .

[0043] And assemble them into a complete tree representation: ; To maintain the consistency of the hierarchical structure, a soft structure consistency penalty term is defined to constrain the hierarchy: ; in, Tree node The parent node, This is used to penalize situations where the range of a child node exceeds that of its parent node. Used to punish situations where there is excessive overlap between sibling nodes.

[0044] S23. Simultaneously optimize structural quality and reference quality indicators: set up Let represent the evaluation scores of a tree in the dimensions of coverage, organization, and relevance of a large language model (LLM). , represents the reference precision and recall of all node-reference pairs in Natural Language Inference (NLI), respectively.

[0045] Define the comprehensive scoring function This invention defines and updates the model by providing feedback to the model for low-scoring dimensions. : ; in, These are preset weight hyperparameters.

[0046] The loop terminates when the following iteration conditions are met, yielding a refined hierarchical knowledge tree: ; in, , The threshold value is set.

[0047] Unlike previous processes that treated classification as an unverified intermediate product, the Multi-View Structured Review System (MVSS) of this invention explicitly optimizes the tree structure itself, ensuring that the hierarchical structure is both structurally meaningful and faithful to the citations.

[0048] S3. Once the refined tree K is available, the Multi-View Structured Review (MVSS) will generate a structured comparison table T, making the methodological comparisons clear, specifically: Predefine a set of table schemas For example, methods-datasets-metrics, tasks-benchmarks-trends, etc., for each tree node This invention uses a pattern selection model. The table schema corresponding to each node of the refined hierarchical knowledge tree Rate it: ; Before screening Each mode This filtering method ensures that the generated tables are semantically aligned with the granularity and research focus of each subtree, rather than setting table columns arbitrarily without basis.

[0049] Cell populations with evidence constraints: for tree nodes Selection mode at the location Definition table ,surface row correspondence method Column corresponding attributes ,surface Each cell They were all generated under a constrained decoding mechanism, which only allows the retention of... Supported content : .

[0050] This invention evaluates the factuality of a table using Natural Language Inference (NLI) fact scores: ; in, It is a set of non-empty cells; correct low-scoring cells. This represents the atomic statements derived from the cell text content of a structured comparison table. Indicates starting from the current tree node Related collection of papers The references selected in the text are evidence.

[0051] This mechanism transforms the tree structure into a framework for systematic, evidence-based comparisons, addressing the lack of structured, cross-paper analysis in previous systems.

[0052] S4. Generate a coherent text summary based on cross-format alignment, specifically: The present invention, the Multi-View Structured Overview System (MVSS), generates a text overview that is explicitly aligned with a sub-knowledge tree K and a structured comparison table T. .

[0053] Text review Previous chapters and selected tree nodes Related, for each node A chapter is generated by using node summaries, local tables, and citations as conditions: .

[0054] The entire review is based on The indicated assembly sequence is as follows: .

[0055] To ensure consistency between tree structures, tables, and text, cross-format alignment is also required: This invention defines a cross-format alignment score: ; in, Used to determine whether a table row or column is anchored to the corresponding tree structure node. This concept is used to measure whether the content of a chapter follows a tree-like structure. This is used to measure whether statements in the text are consistent with table entries, such as through natural language inference (NLI) between sentences and cells.

[0056] At the prompt level, the hierarchical knowledge tree K, comparison table T, and text summary are iteratively corrected based on cross-format aligned scores. Until cross-format aligned scores Stable, the final output is a multi-view structured overview with cross-format aligned scores. The expression is as follows: ; in, 、 These are the weighting coefficients.

[0057] Conceptually, this invention shifts the focus to structure as the primary optimization objective. Unlike processes that merely "use a tree structure and then discard it," MVSS (Multi-View Structured Search System) emphasizes that hierarchical structures, tables, and narratives must be mutually constrained through shared evidence and aligned objectives. This directly addresses the core failure modes mentioned in the introduction and related work sections: single-model structural bias, weak semantics in title-level classification, lack of tree structure quality assessment, and the difficulty in navigating and comparing plain-text reviews across methods.

[0058] Example 1: In this invention, the Multi-View Review System (MVSS) first retrieves topic-related literature and extracts candidate outlines from a heterogeneous large language model (LLM). A stable high-level structure is selected through a calibration consensus procedure, and then, guided by structure and citation-aware evaluation signals, a hierarchical knowledge tree is iteratively constructed and refined. The refined tree serves as the skeleton for generating a structured comparison table, with node-level evidence further constraining the content of table cells to ensure factual basis. Finally, MVSS generates an aligned text review, with each part conditioned on the corresponding tree nodes and tables through cross-format consistency objectives. This design produces three mutually reinforcing views—the tree, the table, and the text—that are coherent, analyzable, and faithfully supported by the underlying literature.

[0059] This embodiment conducted comprehensive experiments to evaluate the Multi-View Review System (MVSS) and its Hierarchical Knowledge Tree (HKT) module from the following three perspectives: (1) the quality of the generated knowledge tree, (2) the quality of the structured comparison table, and (3) the overall review quality when the tree and table are used together to guide text generation.

[0060] Datasets and Corpus: This embodiment evaluates 50 computer science topics covering machine learning, natural language processing, computer vision, and systems research. The retrieved corpus contains 530,000 arXiv papers from 2018 to 2024 and has been preprocessed according to standard scientific literature retrieval practices. Unless otherwise stated, MVSS uses GPT-4o as the primary generator, Claude-3-Sonnet and Gemini-2.5-Pro ​​as heterogeneous models in the multi-model outline stage, and serves as the default Large Language Model (LLM) evaluator in the automatic evaluation of this invention.

[0061] Table 1 Tree Quality Assessment Standards Used by LLM Judges

[0062] Following recent research on automated evaluation of long text generation, this embodiment relies on calibrated LLM judges to score tree diagrams, tables, and reviews. All prompts are consistent with human-written guidelines and scoring is calibrated using a small, expert-annotated dataset, echoing the finding that large language models, when properly designed and validated against human judgment, can serve as reliable automated evaluators. To reduce self-biasing, this embodiment decouples the generative and judging models (GPT-4o never evaluates its own output) and validates the consistency between LLM-based scoring and human scoring in a dedicated human evaluation study.

[0063] Tree quality: This embodiment uses four 5-point criteria: coverage, structure, relevance, and saliency alignment—summarized in Table 1. Given scores , , , The overall tree quality is: .

[0064] Citation quality of trees and reviews: After verification by scientific facts, this embodiment extracts a set of statements. and the model's proposed (declaration, reference) pair If r supports c, then the NLI model... Return 1. The recall and precision are: ; .

[0065] Table Quality: For comparison tables, this embodiment scores them on a 5-point scale, using three aspects: coverage (how many important methods / datasets / metrics are included), discrimination (whether rows / columns show meaningful differences), and readability (the clarity and consistency of the table structure). The average of these three aspects is denoted as follows: .

[0066] Review Quality: For complete reviews, this embodiment uses a Large Language Model (LLM) evaluator to assess the following aspects: coverage, structural coherence, comparative insight (the degree of analysis of similarities / differences), and fidelity to evidence. The average score is denoted as Qsurvey. Furthermore, this embodiment also reuses Reccite ​​and Preccite, calculated based on the review's claims.

[0067] This example is compared with the tree generation and overview generation baselines.

[0068] Human experts: expert-written reviews and their manually extracted hierarchical structure; used as an upper bound.

[0069] Naive Tree Generation: A single-pass GPT-4o hint that directly outputs a hierarchical tree of topics without requiring multi-model consensus or refinement.

[0070] AutoSurvey: A representative automated survey system that first generates an outline and then expands it into text containing sentence-level quotations.

[0071] STORM: A recently emerged framework for generating structured reviews that retrieves substructures of a specific topic before generating a linear narrative. STORM provides stronger structural prior knowledge than AutoSurvey, but lacks the cross-view alignment and joint optimization capabilities used in MVSS.

[0072] AutoSurvey+TreePrompt: AutoSurvey uses a knowledge tree generated by MVSS to provide hints, but does not use tables.

[0073] AutoSurvey+Tree+TablePrompt: AutoSurvey provides prompts using both MVS trees and tables.

[0074] The present invention provides a multi-view overview system MVSS: a complete multi-view structured overview framework as described above, wherein outlines, tree diagrams, tables, and text are jointly generated and optimized.

[0075] Manually generated tree diagrams and summaries used for evaluation have been excluded from the retrieved corpus to avoid information leakage.

[0076] Unless otherwise stated, this embodiment retrieves 1,200 papers related to the topic, generates outlines using abstracts, and retrieves 60 papers for each tree node or table row. The reflection loop of the tree module runs for 3 iterations, and according to... The best candidate was selected based on the reference metrics. The temperature for all API calls was fixed at 1.0; other hyperparameters are listed in the appendix.

[0077] Tree-level evaluation of the HKT module: This embodiment first evaluates the HKT module separately to understand MVSS's ability to construct hierarchical knowledge structures.

[0078] 1) The impact of tree complexity: As shown in Table 2 (mean ± standard deviation of 50 topics), a significant speed-quality tradeoff emerges when the number of top-level parts changes from 2 to 8: deeper trees slow down generation but continuously improve performance. And reference metrics; the eight-part tree achieves optimal balance and is used in subsequent experiments.

[0079] 2) Comparison with the tree-like baseline: Table 3 compares the results of HKT with manually constructed tree diagrams and simple single-step generation. HKT achieves comparable quality to manually constructed tree diagrams, while being several orders of magnitude faster, and its accuracy and consistency are far superior to simple generation. This verifies... Multi-model consensus and bi-objective optimization effectively address the structural instability and reference drift issues in previous tree-based processes.

[0080] 3) Effect of Model Outline Consensus: To evaluate the impact of multi-model outline consensus independently, this embodiment compares MVSS with a variant that uses a single large language model (LLM) (GPT-4o) to generate high-level outlines without cross-model voting. Table 4 shows a tree-level metric and an outline stability measure, defined as the average pairwise similarity between outlines generated under different random seeds.

[0081] Table 2. Impact of the number of segments on HKT performance

[0082] Table 3. Tree-level comparison among HKT, Human Experts, and Naive Generation

[0083] Table 4 Ablation experiments for multi-model profile consensus

[0084] "Contour stability" is the average pairwise similarity (higher is better) between contours generated under different random seeds. This invention's multi-model consensus improves structural quality, strengthens the citation base, and significantly stabilizes high-level decomposition. This demonstrates that using heterogeneous large language models (LLMs) for outline voting is crucial for constructing robust Hong Kong Knowledge Graphs (HKTs).

[0085] End-to-end review evaluation: This embodiment now evaluates MVSS as a complete review generator and analyzes how tree diagrams and tables affect the generated text.

[0086] 1) Comparison with AutoSurvey, STORM, and structural variants: This invention compares four different survey generation configurations in terms of how structural signals are used: AutoSurvey: A standard process that uses only its own profiles and retrievals (without HKT or tables).

[0087] STORM: A structured overview baseline with predefined substructures, but without cross-view alignment or joint optimization.

[0088] AutoSurvey+TreePrompt: AutoSurvey will also receive a knowledge tree generated by MVSS as a structured prompt.

[0089] AutoSurvey+TreeTablePrompt: AutoSurvey receives MVSS trees and tables generated by MVSS as structured hints.

[0090] In this invention, MVSS (Model-View-Side Platform) is used to generate outlines, tree diagrams, and tables, which are then used throughout the retrieval, planning, and validation processes. Table 5 shows the citation and summary quality indicators for these settings. Here, O, T, and Tb represent the availability of Outline, Tree, and Table, respectively.

[0091] Table 5. Overview-level comparisons among AutoSurvey variants, STORM, and MVSS.

[0092] Adding the tree from this invention as a cue improved AutoSurveys' coverage and citation metrics, demonstrating the benefit of an explicit hierarchical structure even when the generation process remained unchanged. Further addition of tables provides stronger comparative analysis, as key dimensions (method, dataset, metrics, and trends) are explicitly aligned. STORM benefits from a stronger structural prior than AutoSurvey but remains limited by its linear generation paradigm and lack of cross-view functionality. Figure 1Consistency. MVSS still outperforms all variants, especially in terms of comparative insights and overall Qsurvey. This suggests that simply injecting structural cues into a linear text flow is insufficient: trees and tables must be generated alongside the overview and used throughout the retrieval, planning, and validation processes, as is done in MVSS.

[0093] 2) Comparison with manually compiled reviews: Finally, this embodiment uses the same LLM evaluation protocol to compare the reviews generated by MVSS with 50 expert-written reviews. Table 6 shows the citation counts and review quality scores.

[0094] As can be seen, the Multiview Review System (MVSS) achieved over [percentage missing] across all review dimensions. This achieves human-level performance while being several orders of magnitude faster. Combining tree-level and table-level results, these experiments demonstrate that multi-view structured review generation is both feasible and necessary to keep pace with rapidly evolving research fields.

[0095] Table 6. Comparison between MVSS-generated reviews and manually written reviews

[0096] Table 7. Results of manual evaluation (out of 1-5 points)

[0097] As shown in Table 7, the MVSS method of this invention achieves the quality of expert-written data and is significantly superior to the automatic baseline method. To keep pace with the rapidly evolving research field, innovation is both feasible and necessary. Manual evaluation is also required. To supplement large language model (LLM)-based assessments and mitigate potential evaluation bias, this invention conducted manual studies on 30 randomly sampled topics. Three PhD-level annotators used a Likert scale of 1-5 to rate the generated reviews on four dimensions: (1) structure and organization, (2) comparative insight, (3) citation accuracy, and (4) overall usefulness.

[0098] Annotators unanimously agreed that the output of this invention's MVSS was preferred over all automated baselines, particularly in terms of comparative insight and conceptual clarity. The high consistency between human scoring and scoring based on large language models (LLMs) further demonstrates the reliability of this invention's automated evaluation protocol.

[0099] Cost Analysis: This invention ultimately compares the computational and monetary costs of MVSS with baseline systems. Table 8 reports the API cost (estimated using publicly available pricing) and actual end-to-end time for each topic.

[0100] Because Multi-View Structured Abstraction (MVSS) involves a cross-view refinement step, its computational cost is slightly higher than that of pipelined baseline methods. However, the improvements in structural clarity, relative completeness, and citation fidelity (see Tables 2, 5, and 7) significantly outweigh this additional overhead, indicating that the structure-first MVSS generation method achieves a good trade-off between accuracy and cost for high-risk review applications.

[0101] Therefore, this invention employs the aforementioned multi-view structured review generation method, a unified framework for generating multi-perspective structured reviews. It elevates conceptual structure to a primary objective, rather than a secondary outcome. MVSS jointly constructs a hierarchical knowledge tree, a pattern-driven comparison table, and evidence-based narrative text, ensuring structural consistency and alignment across all perspectives. Through multi-model outline consensus and dual-objective refinement, MVSS achieves significant improvements in structural clarity, comparative insight, and citation fidelity across 50 different topics, approaching expert-written quality, while simultaneously increasing efficiency by several orders of magnitude. These findings demonstrate that in rapidly evolving research fields, structure-centric synthesis is both feasible and crucial for scalable, high-quality literature understanding.

[0102] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for generating multi-view structured overviews, characterized in that, Includes the following steps: S1. Evidence retrieval and multimodal structuring; S2. Construct and optimize a hierarchical knowledge tree; S3. Generate a structured comparison table; S4. Generate a coherent text summary based on cross-format alignment.

2. The method for generating a multi-view structured overview according to claim 1, characterized in that, S1 specifically refers to: Given topic query and literature corpus The retrieval enhancement encoder retrieves a collection of papers related to the topic. The expression is as follows: in, For use in executing query vectors With document corpus Semantic matching of document vectors; calculation via backend retrieval. and The dense vector cosine similarity or mixed rating signal of the entries is used to return the previous entries in descending order of score. A collection of relevant documents ; Collection of papers Grouped into bundles of papers: ； in, Represents the candidate set; The total number of elements in the candidate set, i.e., the number of candidates; Prompt a set of heterogeneous large language models Generate candidate hierarchical outlines for each topic, as follows: ； in, Refers to the first One model, For index variables, It is the total number of models, that is, the total number of models used. Processing or integrating different models; Indicates by the first A model The generated output results This represents a generating function or forward propagation process, with the model as input. Contextual information and candidate set The output is the generated result corresponding to this model; The evaluation model assesses each candidate level of outline based on coverage, organization, and relevance, and generates a score vector. ； in, For the scoring dimension or feature dimension, each output is mapped to a... In a 3D vector space; After calculating the total score, the outline of the standard levels is obtained through screening. The formula for calculating the total score is as follows: ； in, Indicates by the first A model The total score of the generated output results It is a loop variable, representing the first... One evaluation indicator, For the first The weight of each evaluation indicator, For the first The outline is in the first Quality scores on each indicator; The outline of the standard hierarchy is denoted as: ； in, It is the optimal review outline ultimately selected by the system, used to guide the generation of the subsequent review text; This is the maximum value operator, which means selecting the index that maximizes the score function value from all candidate sets. .

3. The method for generating a multi-view structured overview according to claim 2, characterized in that, S2 includes the following steps: S21. Perform node-level evidence retrieval: For each text descriptor in the specification hierarchy outline tree nodes Perform node-level evidence retrieval: ； in, To refine the number of iterations, For the first A set of supporting materials retrieved in the next iteration. Used to control exploration-refinement progress: ； when At that time, use the parameters from the exploration phase. ;when At that time, use the parameters of the refining stage. ; Maintain a reference set with diversity and stability constraints. ; in: ； Upper and lower constraints on the number of nodes; S22. Generate parallel nodes and assemble the initial hierarchical knowledge tree: Given a descriptor and Generate node summary : ； Assemble into a complete tree representation: ； Define the hierarchy of soft structure consistency penalty constraints: ； in, Tree node The parent node, This is used to penalize situations where the range of a child node exceeds that of its parent node. Used to punish situations where there is excessive overlap between sibling nodes.

4. The method for generating a multi-view structured overview according to claim 3, characterized in that, S2 also includes: S23. Simultaneously optimize structural quality and reference quality indicators: set up Let represent the evaluation scores of a tree in the dimensions of coverage, organization, and relevance of a large language model (LLM). , These represent the reference precision and recall of all node-reference pairs in Natural Language Inference (NLI), respectively. Define the comprehensive scoring function This is achieved by providing feedback to the model for low-scoring dimensions, thereby defining and updating the model. : ； in, These are preset weight hyperparameters; The loop terminates when the following iteration conditions are met, yielding a refined hierarchical knowledge tree: ； in, , The threshold value is set.

5. The method for generating a multi-view structured overview according to claim 4, characterized in that, S3 specifically refers to: Predefine a set of table schemas For each tree node Using the pattern selection model The table schema corresponding to each node of the refined hierarchical knowledge tree Rate it: ； Before screening Each mode ; For tree nodes Selection mode at the location Definition Table ,surface row correspondence method Column corresponding attributes ,surface Each cell Generated under a constrained decoding mechanism that only allows the retention of... Supported content : ； Factuality of the Fact Score Assessment Table through Natural Language Inference (NLI): ； in, It is a set of non-empty cells; correct low-scoring cells. This represents the atomic statements derived from the cell text content of a structured comparison table. Indicates starting from the current tree node Related collection of papers The references selected in the text are evidence.

6. The method for generating a multi-view structured overview according to claim 5, characterized in that, S4 specifically refers to: Text review Previous chapters and selected tree nodes Related, for each node Chapters are generated by using node summaries, local tables, and citations as conditions: ； The entire review is based on The indicated assembly sequence is as follows: 。 7. The method for generating a multi-view structured overview according to claim 6, characterized in that, S4 also includes cross-format alignment targets: Define cross-format alignment score: ； in, Used to determine whether a table row or column is anchored to the corresponding tree structure node. This concept is used to measure whether the content of a chapter follows a tree-like structure. Used to measure whether the statements in the text are consistent with the table entries; Hierarchical knowledge tree based on cross-format alignment score iterative correction K Comparison Table T and text review Until cross-format alignment scores Stable output, multi-view structured overview, cross-format aligned scores The expression is as follows: ； in, 、 These are the weighting coefficients.

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