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Structured Composition of Verifiable Atomic Insights for Table-to-Report Generation

Teng Lin, Xinyu Liu, Nan Tang

arXiv:2610.03525v1cs.CL

TL;DR

Table-to-report generation needs to connect verifiable findings across tables and analytical perspectives, but sequential exploration can miss cross-dimensional evidence. ComInsight enumerates atomic insights, organizes them in a multi-relational graph, and composes higher-order conclusions; across three benchmarks, it outperforms baselines in factual correctness, novelty, and structural completeness.

  • Problem

    Table-to-report generation must discover and connect valuable findings across tables and analytical perspectives into coherent, traceable evidence chains.

    Source: §I · p. 1

  • Method

    ComInsight enumerates atomic insights, organizes them in a typed multi-relational graph, and applies relation-specific composition to produce higher-order insights with SQL provenance.

    Source: §I · pp. 1–2, B. Multi-Relational Graph Construction · p. 3

  • Results

    ComInsight significantly outperforms existing baselines across InsightBench, DDR-Bench, and T2R-Bench in factual correctness, insight novelty, and structural completeness.

    Source: §I · p. 2, §VI · p. 6

  • Takeaways & Limitations

    Structured composition offers a principled, verifiable paradigm for automated report-oriented data analysis.

    Source: §VI · p. 6

Abstract

from arXiv · show

Table-to-report generation refers to the task of automatically generating article-level analyt- ical reports from relational tables and is an essential capability for automated data science and decision support. Its central challenge lies in systematically discovering verifiable com- posite insights across tables, attributes, and analytical perspectives, and organizing them into coherent, complete, and traceable evidence chains. Existing methods primarily rely on sequential, reactive data agents or direct Large Language Model(LLM) generation. They suffer from exploration bias: early local observations constrain subsequent actions, causing models to focus prematurely on local analyzes and miss cross-table or cross-dimensional evidence. We propose ComInsight, which reformulates insight discovery as the composition of atomic evidences. We first define an atomic insight as the smallest executable analytical unit conforming to a predefined analysis pattern and enumerate all valid atomic insights from database schema and content. These atoms are then organized into a multi-relational insight graph, where nodes represent verified data facts and edges encode logical, temporal, or hierarchical relations. Finally, a set of composition operators systematically fuses atomic nodes into higher-order composite conclusions. Every composite output is accompanied by executable SQL and fine-grained provenance, ensuring full verifiability. Across three benchmarks InsightBench, DDR-Bench, and T2R-Bench, ComInsight consistently outperforms strong baselines in factual correctness, novelty, and structural completeness. We believe ComInsight offers a reliable, efficient, and explainable path toward table-to-report generation.

I. INTRODUCTION

Table-to-report generation must connect findings across tables, attributes, and analytical perspectives into coherent, traceable reports. ComInsight addresses the gap between single-finding systems and composite analysis through structured evidence composition.

I. INTRODUCTION

  • Table-to-report generation requires discovering valuable insights across tables and perspectives, then organizing them into coherent, complete, traceable evidence chains.The task is framed as more than generating fluent text and is considered fundamental to automated data science and decision support.Source: §I · p. 1
  • Composite insights connect multiple atomic findings through explicit relationships, revealing patterns that no individual finding shows alone.The retail example combines five evidence chains from three tables and four analysis patterns.Source: §I · p. 1
  • ComInsight enumerates valid atomic insights, organizes verified facts and relations in a typed evidence graph, then composes higher-order conclusions with executable SQL.The paper identifies five composite types: comparative, correlative, contextual, temporal, and causal.Source: §I · pp. 1–2
  • ComInsight significantly outperforms existing baselines on InsightBench, DDR-Bench, and T2R-Bench in factual correctness, insight novelty, and structural completeness.The introduction reports this result across all three benchmarks.Source: §I · p. 2

II. RELATED WORK

  • Prior table benchmarks focus on atomic tasks, while data-analysis agents demonstrate exploration and single-insight discovery without explicitly composing higher-order insights into reports.The related-work discussion distinguishes both benchmark coverage and agent capabilities.Source: §II · p. 2

III. PRELIMINARIES AND PROBLEM FORMULATION

This section frames table-to-report generation around comprehensive discovery with traceability, factual correctness, and non-redundancy. It defines atomic and composite insights and outlines generation, filtering, and judge-training procedures.

III. PRELIMINARIES AND PROBLEM FORMULATION

  • The task is to produce an article-level report of important, non-redundant, verifiable findings from a relational database, with or without a user query.The stated research goal is comprehensive discovery, composition, and reporting while preserving traceability and factual correctness.Source: §III · p. 2
  • An atomic insight is a smallest executable analytical unit represented by source, pattern type, dimensions, metrics, SQL, result, and description.Its pattern type is a parameterizable query template for one exploration logic.Source: §III · p. 2
  • A composite insight combines at least two atomic insights into a comparative, correlative, contextual, temporal, or causal finding with evidence and confidence.Its evidence contains SQL queries and results, while confidence reflects statistical significance and logical consistency.Source: §III · pp. 2–3
  • The report goals require a complete evidence chain for every insight, support from verifiable atoms and executable SQL, and no duplicate or semantically equivalent findings.Source: §III · p. 3

A. Atomic Insight Representation and Generation

  • The generation pipeline recognizes column semantics, instantiates pattern templates, filters for interestingness, and deduplicates atomic insights.When a query or topic is given, a relevance filter selects the top 50 atoms before the interestingness filter.Source: A. Atomic Insight Representation and… · p. 3
  • Training data comprises 290 instances sampled from InsightBench, DDR-Bench, and T2R-Bench for calibrating significance rules and training the judge.Experts annotated 1,200 interestingness labels and 600 preference pairs for SFT and DPO.Source: A. Atomic Insight Representation and… · p. 3
  • Pattern-specific statistical tests use validation-calibrated thresholds, while an SFT- and DPO-trained Llama-3-8B judge assesses semantic interestingness.The filter removes about 85% of atomic insights while preserving high-value discoveries.Source: A. Atomic Insight Representation and… · p. 3

B. Multi-Relational Graph Construction

ComInsight represents atomic insights as nodes in a graph whose typed edges encode five kinds of relationships. It then composes connected insights into higher-order narratives, including validated causal chains.

B. Multi-Relational Graph Construction

  • Five typed relations connect atomic-insight nodes: comparative, correlative, contextual, temporal, and causal.The first four relation types are undirected; causal edges are directed and require both statistical and LLM validation.Source: B. Multi-Relational Graph Construction · p. 3
  • The first four relation types use type-specific weights: comparative edges encode normalized differences, correlative edges co-movement, contextual edges global-local deviations, and temporal edges adjacent-trend slope differences.Comparative edges are retained if normalized relative difference exceeds 0.15, and correlative edges if weight exceeds 0.6. Contextual weights use slope or magnitude deviation, doubling sign reversals; temporal trends are segmented by PELT with a minimum segment length of three.Source: B. Multi-Relational Graph Construction · p. 3

C. Composite Insight Generation

  • Leiden communities in each undirected relation subgraph yield composites when modularity exceeds 0.3 and community size is at least three.An LLM synthesizes the constituent insights into a narrative reflecting their shared relational pattern.Source: C. Composite Insight Generation · pp. 3–4
  • Causal composites aggregate directed paths of up to Lmax into chains that include the Granger p-value, mechanism score, and confounding risk.Every causal edge on a path must pass both the Granger test and the LLM judge.Source: C. Composite Insight Generation · p. 4
  • A composite value evaluator compares each composite with its constituent atoms to decide whether to retain the composite or the atoms.The final insight set includes generated composites and retained atomic insights.Source: C. Composite Insight Generation · p. 4

D. Report Generation

Report generation filters insights for an optional query, groups related insights into thematic sections, and uses an LLM to produce narratives backed by SQL evidence. The accompanying evaluation uses held-out benchmark test splits and reports benchmark-specific metrics.

D. Report Generation

  • For an optional query, report generation removes insights below an embedding-similarity threshold, groups the rest into thematic sections, and generates narratives with every claim backed by SQL evidence.If no query is supplied, all insights are kept; the report is assembled from the generated sections.Source: D. Report Generation · p. 4

A. Experimental Setup

  • Evaluation uses test sets from InsightBench, DDR-Bench, and T2R-Bench, with training instances excluded from held-out test splits.The benchmarks include 100 datasets and 475 insights, 291 tasks and 2,058 insights, and 910 questions on 457 industrial tables across 19 domains, respectively.Source: A. Experimental Setup · pp. 4–5
  • Baselines include Pandas Agent, AgentPoirot, DeepSeek-R1, Claude 4.5 Sonnet, and GPT-5.5; DeepSeek-R1 scores 62.71% overall on T2R-Bench, while Claude 4.5 Sonnet is the best-performing proprietary model on DDR-Bench.Source: A. Experimental Setup · p. 5
  • The evaluation uses LLaMA-3-Eval for InsightBench, checklist-based accuracy for DDR-Bench, and NAC, ICC, and GEC for T2R-Bench.T2R-Bench reports the average of its three dimensions.Source: A. Experimental Setup · p. 5

1) Implementation Details:

  • Experiments use GPT-5.5 for composite-insight and report generation at temperature 0.0, with each experiment repeated five times and averages reported.The implementation uses NetworkX MultiGraph and eight NVIDIA RTX4090 GPUs.Source: 1) Implementation Details · p. 5

B. Main Results

ComInsight outperforms baselines across the three benchmarks, with reported gains in insight and summary evaluation, checklist accuracy, and T2R-Bench dimensions. Ablations on T2R-Bench associate the largest average-score reduction with removing composite insight generation.

1) Performance on InsightBench:

  • 0.73 insight-level LLaMA-3-Eval on InsightBench exceeds AgentPoirot (gpt-5.5) by 13.3 percentage points; GPT-5.5 scores 0.51.ComInsight also significantly outperforms all baselines on both insight-level and summary-level scores.Source: 1) Performance on InsightBench · p. 5

2) Performance on DDR-Bench:

  • 41.23% accuracy on DDR-Bench’s MIMIC scenario exceeds Claude 4.5 Sonnet by 14.3%, while 10-K reaches 79.34%, 2.2% above the native model.ComInsight achieves the best performance on MIMIC, GLOBEM, and 10-K; GLOBEM rises from 42.31% to 42.87%, with the modest improvement attributed likely to its closed-form checklist items.Source: 2) Performance on DDR-Bench · p. 5

3) Performance on T2R-Bench:

  • 83.5% Average on T2R-Bench exceeds native GPT-5.5’s 64.61% by 18.89 percentage points, and ComInsight outperforms baselines across all three evaluation dimensions.The largest dimensional improvement is +26.48% on Numerical Accuracy Criterion (NAC); Information Coverage Criterion (ICC) rises from 49.75% to 73.73%.Source: 3) Performance on T2R-Bench · p. 5

C. Ablation Studies

  • Removing composite insight generation reduces T2R-Bench Average from 83.5% to 64.61% (∆Avg = −18.89%), the largest reported ablation drop.Removing composite value evaluation yields 71.34%, while removing causal relationship generation yields 76.75%. The value evaluator is described as suppressing redundant or low-utility composites and preserving coverage; causal chains support reasoning depth and narrative completeness.Source: C. Ablation Studies · p. 5

1) Efficiency and Time Analysis:

ComInsight produces atomic and composite insights at measurable pipeline cost, and the conclusion reports gains over baselines across three benchmarks. The paper closes by describing its structured-composition approach and disclosing its use of generative AI.

1) Efficiency and Time Analysis:

  • The pipeline averages about 28 atomic insights, 142 typed edges, and 41 composite insights per task, taking about 102.3 seconds end to end.Composite generation takes 52.8 seconds (51.6%); batching LLM calls and parallelizing that stage can reduce time by 4–6×.Source: 1) Efficiency and Time Analysis · p. 5

VI. CONCLUSION

  • Across InsightBench, DDR-Bench, and T2R-Bench, ComInsight substantially outperforms baselines in factual correctness, novelty, and structural completeness.The conclusion also says ablations confirm critical roles for the multi-relational graph and specialized composition operators.Source: §VI · p. 6
  • ComInsight combines verified atomic insights, a typed evidence graph, relation-specific composition, and query alignment with executable SQL provenance.The conclusion presents this as a structured, verifiable paradigm for report-oriented data analysis.Source: §VI · p. 6

VII. AI USE STATEMENT

  • The authors report using generative AI for specified methodology and evaluation tasks, reviewing AI-assisted work, and taking responsibility for the final content.They state that generated evidence was checked against executable SQL and experimental results.Source: §VII · pp. 6–7

APPENDIX

The appendix describes ComInsight’s progression from atomic insight discovery through graph modeling and composition. It defines atomic insights and details their generation, filtering, evaluation, and deduplication.

APPENDIX

  • ComInsight takes relational tables as input and progresses through atomic discovery, relational modeling, structured composition, and causal verification.The framework is described as three core modules and two auxiliary layers.Source: APPENDIX · p. 9

1) Definition of Atomic Insight:

  • An atomic insight is the smallest executable analytical unit conforming to a predefined analysis grammar.It is retained in the evidence pool only after passing both statistical-salience and semantic-interestingness checks.Source: 1) Definition of Atomic Insight · p. 9
  • The atomic insight representation records its source, analysis type, dimensions, metrics, metadata, executable SQL, result, and description.The module produces a curated set of atomic findings through five stages.Source: 1) Definition of Atomic Insight · p. 9

2) Generation Pipeline for Atomic Insights:

  • Generation classifies columns as dimensions, metrics, or time fields, then instantiates SQL templates across dimension–metric–pattern combinations.The column classification combines statistics with naming heuristics.Source: 2) Generation Pipeline for Atomic… · p. 10
  • Statistical filtering applies pattern-specific rules, including retaining extrema and thresholding grouped averages, sums, counts, correlations, and trends.For correlations, candidates require |r| > 0.6 and p < 0.05; trend candidates require regression p < 0.05.Source: 2) Generation Pipeline for Atomic… · p. 10
  • A lightweight LLM judge evaluates semantic interestingness because statistical significance alone does not guarantee that an insight is interesting, novel, or actionable.The judge is trained using expert annotations from benchmark-derived candidate insights.Source: 2) Generation Pipeline for Atomic… · p. 10
  • The pipeline merges semantically equivalent or near-redundant insights, retaining the item with the most concise description.It normalizes candidates by description, metrics, and type.Source: 2) Generation Pipeline for Atomic… · p. 11

3) Query Sematic Filter:

The passages describe query relevance scoring and two graph-edge weighting schemes. Contextual weights compare local patterns with global baselines, while temporal weights link adjacent time segments.

3) Query Sematic Filter:

  • Intent alignment scores atomic insights for relevance to a user query using text embeddings and cosine similarity, then filters the candidate set.The passage also describes concatenating text inputs for embedding.Source: 3) Query Sematic Filter · p. 11
  • A contextual edge links a global aggregate insight to a local segmented insight, with weight measuring the local pattern’s deviation from the global baseline.The weight uses local and global trend slopes or magnitude values, with a small constant to prevent division by zero.Source: 3) Query Sematic Filter · p. 11
  • A temporal edge links same-metric trend insights across adjacent PELT-partitioned intervals; requiring at least three time points per segment suppresses over-segmentation.Its weight uses the slopes of adjacent intervals and their time-point counts.Source: 3) Query Sematic Filter · p. 11

C. Composite Insight Generation

Composite insight generation filters atomic insights before organizing them into a typed, weighted multi-relational graph. The graph supports comparative and correlative links between findings.

C. Composite Insight Generation

B. Multi-Relational Graph Construction

  • The graph represents filtered atomic insights as nodes and uses comparative, correlative, contextual, and temporal relation types with typed, weighted edges.Its MultiGraph structure permits parallel edges between a node pair to represent different relational facets.Source: B. Multi-Relational Graph Construction · p. 11
  • Comparative edges link findings with the same metric and aggregation but different dimension values, retaining only normalized-difference weights above 0.15.Source: B. Multi-Relational Graph Construction · p. 11
  • Correlative edges connect statistically dependent insights, including correlation and co-moving trend findings; weights above 0.6 are treated as strongly correlative.The passage identifies the Pearson correlation coefficient and its corresponding p-value in the edge calculation.Source: B. Multi-Relational Graph Construction · p. 11

1) Definition of Composite Insight:

ComInsight represents composite insights as evidence-backed combinations of atomic insights and aggregates related atoms into higher-order conclusions. Its community-based procedure groups atoms by relation type before generating and scoring summaries.

1) Definition of Composite Insight:

  • The taxonomy includes comparative, correlative, contextual, temporal, and causal composites.A causal score above τcausal marks a composite as causal.Source: 1) Definition of Composite Insight · p. 11
  • Composite insights comprise a unique ID, one of five types, at least two atoms, a summary, and supporting SQL queries and results.The five types are comparative, correlative, contextual, temporal, and causal.Source: 1) Definition of Composite Insight · p. 11

D. Community-based Aggregation: Implementation Steps

  • Community aggregation builds separate subgraphs for four relation types, retains connected communities with modularity above 0.3 and at least three nodes, then uses an LLM to summarize each.Leiden is used for community detection.Source: D. Community-based Aggregation:… · pp. 11–12
  • The rubric scores whether a composite offers novel synthesis, information gain, and logical coherence.Novel synthesis is scored 0–3, information gain 0–2, and logical coherence 0–2.Source: D. Community-based Aggregation:… · p. 12
  • The rubric scores relational explanation on a 0–2 scale and decision utility on a 0–1 scale, then returns “Yes” when the total score is at least 6 and “No” otherwise.Source: D. Community-based Aggregation:… · p. 12

1) Deduplication and Confidence Scoring:

ComInsight deduplicates composite candidates and retains those meeting its confidence threshold. It then filters and groups insights as needed to generate a traceable article-level report, with basic analysis patterns and SQL templates listed in Table III.

1) Deduplication and Confidence Scoring:

  • Composites with identical atomic ID sets are deduplicated, and only those with confidence above 0.6 are retained.Confidence uses normalized atomic statistical significance and a binary logical-coherence check; indirect composites receive manual audit.Source: 1) Deduplication and Confidence Scoring · p. 12

E. Report Generation

  • Report generation optionally filters composites by query relevance, groups them into thematic sections, and assembles an evidence-referenced report.An LLM generates coherent paragraphs from composites and their evidence; the report includes a title, abstract, and conclusion.Source: E. Report Generation · p. 12

F. Basic Analysis Patterns and SQL Templates

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