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REVIEW 4 major objections 4 minor 106 references

Map&Make: Schema Guided Text to Table Generation

T0 review · 4 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Map&Make claims that text-to-table generation improves sharply when text is decomposed into atomic propositions, the table schema is inferred iteratively, and cells are filled proposition by proposition.

desk verdict A solid prompting pipeline with genuine coverage gains, but the headline Rotowire numbers may be partly an artifact of the authors' own benchmark correction. read the letter →

arxiv 2505.23174 v1 pith:HTONQ6KL submitted 2025-05-29 cs.CL

Naman Ahuja , Fenil Bardoliya , Chitta Baral , Vivek Gupta This is my paper
classification cs.CL
keywords text-to-tablegenerationschemainductionpropositionalatomizationlargelanguagemodelsstructuredsummarizationRotowireLivesumAutoQA
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper claims that turning dense text into a useful table does not require a predefined schema, and that planning the table is the hard part. Its Map&Make method first splits the text into small self-contained factual statements, then grows the table's row and column headers one statement at a time, and finally fills or updates cells statement by statement. On the Rotowire basketball benchmark this raises cell- and column-level character n-gram coverage by up to 32% over chain-of-thought baselines, and on the Livesum football benchmark it cuts error rate by up to 55% and RMSE by up to 57% in the one-shot setting. The authors also release a manually corrected version of Rotowire that removes hallucinated or missing entries they found in prior benchmarks, and they report a 14% AutoQA gain on open-domain Wikipedia articles. If these results hold, structured summarization can work without any fixed target layout, making text-to-table generation more adaptable for information retrieval.

What carries the argument

The load-bearing mechanism is a three-stage prompt pipeline. Propositional Atomization converts each input sentence into atomic, self-contained facts satisfying well-formedness, atomicity, self-containedness, support, and comprehensiveness. Iterative Schema Extraction starts with empty row and column header lists and grows them per statement, mapping entities to rows and attributes to columns, so the table structure emerges from the text rather than from a preset template. Iterative Table Generation then processes every statement to update or fill cells, returning statement-wise updates for transparency. The dynamic schema is what lets the method adapt to multi-table structures such as Rotowire's player and team tables and to counting tasks such as Livesum's event tallies.

What would settle it

Run the same comparison on the original, uncorrected Rotowire test set or on an independently annotated sample of the corrected set: if the reported CHRF gains of up to 32% shrink to near zero, the advantage comes from the label correction rather than from the method. Alternatively, measure inter-annotator agreement on a sample of corrected cells; low agreement would indicate the benchmark changes are partly subjective.

Watch

Extended reading notes

Core claim

The paper's central claim is that the bottleneck in text-to-table generation is planning the table, not extracting facts: models that jump straight from text to a table miss information because they never explicitly decide what the table should contain. Map&Make therefore inserts two planning stages between reading and filling — propositional atomization, which turns the text into self-contained atomic facts, and iterative schema extraction, which grows row and column headers one statement at a time. Only then does it fill the table, updating cell values statement by statement with explicit change records. The authors report that this three-stage procedure raises cell- and column-level CHRF coverage by up to 32% over chain-of-thought baselines on Rotowire, cuts error rate by up to 55% and RMSE by up to 57% on Livesum in the one-shot setting, and achieves a 14% AutoQA gain on open-domain Wiki40B articles, while staying stable as input length and schema size grow. The paper also presents a manually corrected Rotowire test set intended to remove hallucination and missing-information errors from earlier versions of the benchmark.

Load-bearing premise

The load-bearing premise is that the manual corrections the authors made to the Rotowire test set are accurate and unbiased: thousands of cells, rows, and columns were changed using the authors' own judgment about which information is relevant, and the paper reports no inter-annotator agreement and no control evaluation preventing the corrections from favoring Map&Make's tendency to extract additional columns.

Editorial extensions

If this is right

  • A predefined schema is not required: table structure can be planned from the text in zero-shot and one-shot settings, which makes the method applicable to domains where no target layout exists.
  • Longer, denser inputs need not degrade results: Map&Make keeps schema coverage stable as table size grows, where CoT-style prompting misses progressively more columns.
  • Counting tasks can be made reliable by incremental updates: on Livesum, statement-wise filling reduces hallucinated overcounting, which is the dominant error mode of chain-of-thought baselines.
  • The corrected Rotowire test set gives future work a benchmark whose entries are grounded in the source text rather than inherited from earlier versions.
  • The method's ability to emit several tables (up to 13 on Wiki40B) suggests multi-table summaries can be produced without asking for a fixed number of tables in advance.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper, the same atomize-schema-fill loop is a state-update formulation of summarization; it could transfer to other structured outputs such as knowledge graphs or event logs, where each atomic fact becomes an edge or a timestamped event.
  • Because M&M also adds non-statistical columns that are absent from Rotowire's gold tables, reference-based precision scores penalize it; a natural next step would be metrics that reward faithful extra information, as AutoQA already does.
  • The reported gains would be tested most directly by an independent re-annotation of a Rotowire sample: if the corrected labels are not reproducible, part of the advantage over baselines could be an artifact of label changes.
  • A promising extension is to feed the atomic statements and evolving schema back into the same model in a single pass for long documents, trading some transparency for lower computational cost.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper introduces Map&Make (M&M), a three-stage prompting framework for schema-agnostic text-to-table generation: propositional atomization, iterative schema extraction, and iterative table filling. The method is evaluated on Rotowire, using a manually corrected test set introduced by the authors, and on Livesum, an external benchmark requiring event counting and aggregation; a zero-shot Wiki40B study is included for open-domain generalization. Against CoT and Text-Tuple-Table baselines across GPT-4o, Gemini 2.0 Flash, and Llama 3.3 70B, the paper reports large coverage gains on Rotowire (up to 32% CHRF improvements) and large reductions in Livesum error rate and RMSE, alongside ablations and qualitative error analyses. The central claim is that M&M outperforms existing methods and generalizes robustly across tabular summarization paradigms.

Significance. If the results are sound, the paper makes a useful contribution: the modular pipeline is transparent, the appendix provides full prompts, code and data are linked, and the ablation studies isolate the contribution of atomization and iterative schema construction. The Livesum results are particularly valuable because that benchmark is not altered by the authors and the one-shot gains on hard columns are substantial. The Wiki40B study, despite being reference-less, suggests the method transfers beyond sports text. However, the central Rotowire claim is compromised by the fact that all Rotowire comparisons use a test set that was manually corrected by the authors without inter-annotator agreement and without a control evaluation on the original benchmark; the correction adds exactly the kind of statistical columns M&M tends to over-generate. The paper also overstates consistency across models and metrics, since several zero-shot and some one-shot configurations show correctness regressions, and no error bars or significance tests are reported. These issues are fixable but currently prevent full confidence in the headline claim.

major comments (4)
  1. [Section 3.1, Table 1, Appendix A.2.1] The manually corrected Rotowire test set is not validated. The authors report changing thousands of rows, columns, and cells based on their own judgment, adding the column headers 'Points in the Paint' and 'Half-Time Score' and restricting the ground truth to statistical values relevant to the match. No inter-annotator agreement, no correction-protocol validation, and no evaluation on the original Wu et al. (2022) test set are provided. Because M&M is specifically designed to produce additional columns (Appendix B.1), the corrected ground truth may systematically reward M&M's coverage behavior on the column-level metrics where the largest gains are reported (Table 2, CHRF column +32%). A control evaluation on the original test set or an independent re-annotation study is required before the claim that M&M 'outperforms existing methods' on Rotowire can be independently tested.
  2. [Section 5, Table 2, Table 3] The claim that M&M improves 'correctness significantly across all models and evaluation metrics' is not supported by the paper's own tables. For Llama-3.3 70B in the zero-shot setting, M&M-U has lower cell EM (19.26 vs. 21.44), lower cell CHRF (35.63 vs. 40.79), and lower column CHRF (45.45 vs. 51.40) than CoT. In Table 3, GPT-4o one-shot TabEval Team correctness drops from 80.23 (CoT) to 66.28 (M&M-U) and to 48.78 (M&M-3S). The paper acknowledges a correctness decrease in Appendix B.1, but the main text should state the result as a coverage/completeness improvement with known precision trade-offs, not as uniform outperformance.
  3. [Section 4.1-4.2, Tables 2-4] No repeated runs, error bars, or significance tests are reported, although the experiments use stochastic LLM sampling. The stated gains, some of which are modest relative to baselines, may be within run-to-run variance. The authors should report multiple runs with means and standard deviations, or at least paired bootstrap or significance tests, for the headline metrics in Tables 2-4. Keeping temperature, top_p, and top_k constant does not remove sampling stochasticity.
  4. [Section 4.2, Eq. (1), Table 3, Appendix B.1, Limitations] AutoQA is used as the main evidence that M&M's extra columns are faithful to the text, but it is an LLM-based reference-less metric: the QA pairs are generated by an LLM from the input text and the answers are evaluated by an LLM from the generated tables. The paper's own Limitations section concedes that 'a subjective human evaluation is needed.' Without validation of the QA pairs or a human assessment of the extra columns, AutoQA cannot resolve the precision/coverage trade-off that the corrected Rotowire benchmark also tends to mask. The authors should provide gold or human-validated QA pairs, or a human evaluation of the added columns.
minor comments (4)
  1. [Appendix A.2.1, Appendix E.4.1] The correction section states that the full paraphrasing prompt 'can be found in appendix E.4.1,' but Appendix E.4.1 contains the propositional atomization prompt; the referenced paraphrasing prompt appears to be missing.
  2. [Section 5.4, Table 5] The text says CoT misses an average of 0.05 Team rows per table, but Table 5 lists 0.05 under Extra Information and 0.019 under Missing Information for CoT Team Rows; the text and table should be aligned.
  3. [Figure 3] The label 'Uncercounted' in Figure 3 should be 'Undercounted.'
  4. [Section 4.2 and Eq. (1)] There is a typo ('Additionaly') in Section 4.2, and the expectation notation E_{(q_i,a_i)} in Eq. (1) is nonstandard; define it as an average over the QA pairs generated from the source text.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: Map&Make's empirical claims are not equivalent to its inputs by construction.

full rationale

Map&Make is an empirical prompting pipeline rather than a derived mathematical quantity, and no fitted parameter is renamed as a prediction. The three-stage design (propositional atomization, iterative schema extraction, iterative table filling) is defined independently of the Rotowire and Livesum references, and the reported gains are measured with standard string-similarity, TabEval, and AutoQA metrics. The manually corrected Rotowire benchmark (Section 3.1 and Appendix A.2.1) is a legitimate external-validity concern: the authors revise the test reference by hand, add two column headers, and provide no inter-annotator agreement or evaluation on the original Wu et al. (2022) test set. However, this is not a circular step under the stated definitions because the correction is not a function of Map&Make's outputs and does not mathematically force the reported CHRF or TabEval gaps. Similarly, AutoQA and TabEval are reference-less LLM-based metrics that share the source text with the generated tables; this can introduce evaluation bias, but the metrics are not defined in terms of Map&Make's predictions. The paper contains no load-bearing self-citations, no imported uniqueness theorems, and no renamed known results. The Appendix C fine-tuning study uses Gemini 2.0 to generate both supervision data and AutoQA pairs, which is self-referential in evaluation, but that study is auxiliary and not the central claim of the paper. Overall, the central empirical assertions rest on an independent experimental setup, so no derivation chain reduces to its own inputs by construction.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The central claim depends on a stack of LLM behaviors (atomization, schema planning, cell filling) and on a benchmark the authors corrected manually. None of these are independently verified in the paper, and the AutoQA validation is itself LLM-based, so the ledger contains several unstated assumptions rather than conventional fitted parameters.

assumptions (4)
  • domain assumption An LLM can faithfully decompose text into atomic statements without adding or omitting information.
    The entire method relies on the atomization prompt (Appendix E.4.1) producing comprehensive and faithful facts; the paper's ablations show dropping this step hurts coverage, so its correctness is load-bearing.
  • domain assumption The iterative schema extraction, based on atomic statements, discovers the latent table structure.
    The schema step (Section 2.2) assumes that processing statements one by one yields complete row and column headers; failures here directly cause missing columns.
  • ad hoc to paper The manually corrected Rotowire ground truth is the correct target.
    The paper introduces a new version of Rotowire (Section 3.1) with thousands of corrections made by the authors; no inter-annotator agreement or external validation is provided.
  • domain assumption LLM-generated AutoQA questions and answers provide a valid reference-less measure of table quality.
    Section 4.2 uses AutoQA to validate informational coverage, including extra columns; this metric depends on the evaluation LLM and could be biased.

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Cite this review

Pith. "Pith review of Map&Make: Schema Guided Text to Table Generation." pith.science (2026). https://pith.science/paper/HTONQ6KL

@misc{pith2026250523174,
  author       = {Pith},
  title        = {Pith review of: Map&Make: Schema Guided Text to Table Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HTONQ6KL}},
  note         = {Machine review of arXiv:2505.23174}
}
read the original abstract

Transforming dense, detailed, unstructured text into an interpretable and summarised table, also colloquially known as Text-to-Table generation, is an essential task for information retrieval. Current methods, however, miss out on how and what complex information to extract; they also lack the ability to infer data from the text. In this paper, we introduce a versatile approach, Map&Make, which "dissects" text into propositional atomic statements. This facilitates granular decomposition to extract the latent schema. The schema is then used to populate the tables that capture the qualitative nuances and the quantitative facts in the original text. Our approach is tested against two challenging datasets, Rotowire, renowned for its complex and multi-table schema, and Livesum, which demands numerical aggregation. By carefully identifying and correcting hallucination errors in Rotowire, we aim to achieve a cleaner and more reliable benchmark. We evaluate our method rigorously on a comprehensive suite of comparative and referenceless metrics. Our findings demonstrate significant improvement results across both datasets with better interpretability in Text-to-Table generation. Moreover, through detailed ablation studies and analyses, we investigate the factors contributing to superior performance and validate the practicality of our framework in structured summarization tasks.

Figures

Figures reproduced from arXiv: 2505.23174 by the authors.

Figure 1
Figure 1. Comparison between Naive methods and our [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. An illustration of our approach. Propositional Breakdown segments the text to generate atomic statements [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 4
Figure 4. Comparison of Schema-Coverage with Increasing Table Sizes for Rotowire [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figures from the paper (3 more)
Figure 3
Figure 3. Figure 3: RMSE of Overcounting and Undercounting In￾stances for Livesum. Uncercounted refers to cell values less than the ground truth, Overcounted refers to cell values more than the ground truths. Livesum: We further segregate instances of over￾counting and undercounting of ev…
Figure 5
Figure 5. Figure 5: Eight types of event information (inner circle) that require summarization in Livesum dataset, along with their common expressions (outer circle) in the commentary. (Deng et al., 2024) A.2 Rotowire The Rotowire dataset was originally introduced by (Wiseman et al., 2017…
Figure 6
Figure 6. Figure 6: Instances of hallucinations and missing information in Rotowire. [PITH_FULL_IMAGE:figures/full_fig_p014_6.png]

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Reference graph

Works this paper leans on

106 extracted references · 77 canonical work pages

  1. [1]

    Team" table - Row added:

    The Hornets ' record is 5 wins. *Schema update*: - Update in "Team" table - Row added: "Hornets" - Column added: "Wins"

  2. [2]

    Team" table - Row added: None (

    The Hornets ' record is 15 losses. *Schema update*: - Update in "Team" table - Row added: None ("Hornets" is already present in the schema) - Column added: "Losses"

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    gTBLS: Generating Tables from Text by Conditional Question Answering

    Stable: Table generation framework for encoder-decoder models. In Proceedings of the 18th Conference of the European Chapter of the Associa- tion for Computational Linguistics (Volume 1: Long Papers), pages 2454–2472. Maja Popovi´c. 2015. chrF: character n-gram F-score for automatic MT evaluation. In Proceedings of the Tenth Workshop on Statistical Machin...

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    llmner:(zero| few)-shot named entity recogni- tion, exploiting the power of large language models. arXiv preprint arXiv:2406.04528. 11 Lucy Lu Wang, Kyle Lo, Yoganand Chandrasekhar, Rus- sell Reas, Jiangjiang Yang, Douglas Burdick, Darrin Eide, Kathryn Funk, Yannis Katsis, Rodney Kinney, et al. 2020a. Cord-19: The covid-19 open research dataset. ArXiv. Zh...

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    Points in the Paint

    Challenges in data-to-document generation. In Proceedings of the 2017 Conference on Empiri- cal Methods in Natural Language Processing, pages 2253–2263, Copenhagen, Denmark. Association for Computational Linguistics. Xueqing Wu, Jiacheng Zhang, and Hang Li. 2022. Text- to-Table: A New Way of Information Extraction. In Proceedings of the 60th Annual Meetin...

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  6. [6]

    Team" table - Row added: None - Column added:

    The Knicks have a seven-game losing streak. *Schema update*: - Update in "Team" table - Row added: None - Column added: "Losing streak"

  7. [7]

    Team" table - Row added: None - Column added:

    The Hornets defeated the Knicks 103-102 on Friday. *Schema update*: - Update in "Team" table - Row added: None - Column added: "Points scored"

  8. [8]

    Team" table - Row added: None - Column added:

    The Hornets snapped a 10-game winning streak. *Schema update*: - Update in "Team" table - Row added: None - Column added: "Winning streak"

Show all 106 references
  1. [9]

    Team" table - Row added:

    The Knicks ' record is 4 wins. *Schema update*: - Update in "Team" table - Row added: "Knicks" - Column added: None ("Wins" is already present in the schema)

  2. [10]

    Team" table - Row added: None (

    The Knicks ' record is 17 losses. *Schema update*: - Update in "Team" table - Row added: None ("Knicks" is already present in the schema) - Column added: None ("Losses" is already present in the schema)

  3. [11]

    Player" table - Row added:

    Carmelo Anthony made a three-point shot. *Schema update*: - Update in "Player" table - Row added: "Carmelo Anthony" - Column added: None ("Three-point shots made" is already present in the schema)

  4. [12]

    *Schema update*: - Update in "Team" table - Row added: None - Column added: None

    The Knicks were within two points of Charlotte. *Schema update*: - Update in "Team" table - Row added: None - Column added: None

  5. [13]

    Team" table - Row added: None - Column added:

    Charlotte led by 14 points entering the fourth quarter. 18 *Schema update*: - Update in "Team" table - Row added: None - Column added: "Lead entering fourth quarter"

  6. [14]

    *Schema update*: - Update in "Team" table - Row added: None - Column added: None

    The Knicks reduced Charlotte 's lead in the final four minutes. *Schema update*: - Update in "Team" table - Row added: None - Column added: None

  7. [15]

    Player" table - Row added:

    J.R. Smith made a three-point shot. *Schema update*: - Update in "Player" table - Row added: "J.R. Smith" - Column added: "Three-point shots made"

  8. [16]

    Team" table - Row added: None - Column added:

    The Hornets had possession of the ball with four seconds to play. *Schema update*: - Update in "Team" table - Row added: None - Column added: "Possession with 4 seconds"

  9. [17]

    Player" table - Row added:

    The ball was inbounded to Kemba Walker. *Schema update*: - Update in "Player" table - Row added: "Kemba Walker" - Column added: None

  10. [18]

    Player" table - Row added: None (

    Carmelo Anthony made another three-point shot. *Schema update*: - Update in "Player" table - Row added: None ("Carmelo Anthony" is already present in the schema) - Column added: None

  11. [19]

    Team" table - Row added: None - Column added:

    The Knicks had a one-point advantage with 40 seconds to play. *Schema update*: - Update in "Team" table - Row added: None - Column added: "Advantage with 40 seconds"

  12. [20]

    *Schema update*: - Update in "Team" table - Row added: None - Column added: None

    Both teams missed baskets. *Schema update*: - Update in "Team" table - Row added: None - Column added: None

  13. [21]

    Player" table - Row added:

    Gerald Henderson scored 22 points. *Schema update*: - Update in "Player" table - Row added: "Gerald Henderson" - Column added: None ("Points" is already present in the schema)

  14. [22]

    Player" table - Row added:

    Al Jefferson scored 16 points. *Schema update*: 19 - Update in "Player" table - Row added: "Al Jefferson" - Column added: None ("Points" is already present in the schema)

  15. [23]

    Player" table - Row added: None (

    Kemba Walker drove into the paint. *Schema update*: - Update in "Player" table - Row added: None ("Kemba Walker" is already present in the schema) - Column added: None

  16. [24]

    Player" table - Row added: None - Column added:

    Kemba Walker scored at the buzzer. *Schema update*: - Update in "Player" table - Row added: None - Column added: "Buzzer beater"

  17. [25]

    Player" table - Row added: None - Column added:

    Kemba Walker scored his 11th point of the game. *Schema update*: - Update in "Player" table - Row added: None - Column added: "Points"

  18. [26]

    Player" table - Row added: None - Column added:

    Lance Stephenson had six assists. *Schema update*: - Update in "Player" table - Row added: None - Column added: "Assists"

  19. [27]

    Player" table - Row added: None - Column added: None (

    Lance Stephenson had six rebounds. *Schema update*: - Update in "Player" table - Row added: None - Column added: None ("Rebounds" is already present in the schema)

  20. [28]

    Player" table - Row added: None - Column added:

    Al Jefferson had 13 rebounds. *Schema update*: - Update in "Player" table - Row added: None - Column added: "Rebounds"

  21. [29]

    Player" table - Row added:

    Lance Stephenson scored 16 points. *Schema update*: - Update in "Player" table - Row added: "Lance Stephenson" - Column added: None ("Points" is already present in the schema)

  22. [30]

    Player" table - Row added: None - Column added:

    Lance Stephenson made 7 of 15 field goal attempts. *Schema update*: - Update in "Player" table - Row added: None - Column added: "Field goal attempts"

  23. [31]

    Team" table - Row added: None - Column added:

    The Hornets made 4 of 18 three-point attempts. *Schema update*: - Update in "Team" table - Row added: None - Column added: "Three-point attempts"

  24. [32]

    Player" table - Row added: None (

    Carmelo Anthony scored 32 points. *Schema update*: - Update in "Player" table - Row added: None ("Carmelo Anthony" is already present in the schema) - Column added: None ("Points" is already present in the schema)

  25. [33]

    Player" table - Row added:

    Gary Neal scored 13 points off the bench. *Schema update*: - Update in "Player" table - Row added: "Gary Neal" - Column added: None ("Points" is already present in the schema)

  26. [34]

    Team" table - Row added: None - Column added:

    The Hornets shot 48 percent from the floor. *Schema update*: - Update in "Team" table - Row added: None - Column added: "Shooting percentage"

  27. [35]

    Team" table - Row added: None - Column added:

    The Hornets made 22 percent of their three-point attempts. *Schema update*: - Update in "Team" table - Row added: None - Column added: "Three-point percentage"

  28. [36]

    Player" table 20 - Row added: None (

    J.R. Smith scored 14 points. *Schema update*: - Update in "Player" table 20 - Row added: None ("J.R. Smith" is already present in the schema) - Column added: None ("Points" is already present in the schema)

  29. [37]

    Player" table - Row added: None - Column added: None (

    J.R. Smith had five assists. *Schema update*: - Update in "Player" table - Row added: None - Column added: None ("Assists" is already present in the schema)

  30. [38]

    Player" table - Row added: None - Column added: None (

    Carmelo Anthony had seven rebounds. *Schema update*: - Update in "Player" table - Row added: None - Column added: None ("Rebounds" is already present in the schema)

  31. [39]

    Player" table - Row added: None - Column added: None (

    Carmelo Anthony had four assists. *Schema update*: - Update in "Player" table - Row added: None - Column added: None ("Assists" is already present in the schema)

  32. [40]

    Player" table - Row added:

    Tim Hardaway, Jr. scored 13 points. *Schema update*: - Update in "Player" table - Row added: "Tim Hardaway, Jr." - Column added: None ("Points" is already present in the schema)

  33. [41]

    Player" table - Row added:

    Samuel Dalembert committed three turnovers. *Schema update*: - Update in "Player" table - Row added: "Samuel Dalembert" - Column added: "Turnovers"

  34. [42]

    Team" table - Row added: None - Column added:

    The Knicks will host the Blazers on Sunday. *Schema update*: - Update in "Team" table - Row added: None - Column added: "Next game"

  35. [43]

    Player" table - Row added: None - Column added: None (

    J.R. Smith had four rebounds. *Schema update*: - Update in "Player" table - Row added: None - Column added: None ("Rebounds" is already present in the schema)

  36. [44]

    Team" table - Row added: None - Column added:

    The Knicks were out-rebounded 41 to 34. *Schema update*: - Update in "Team" table - Row added: None - Column added: "Rebounds"

  37. [45]

    Team" table - Row added: None - Column added:

    The Knicks committed six turnovers. *Schema update*: - Update in "Team" table - Row added: None - Column added: "Turnovers"

  38. [48]

    *Schema update*: - Update in "Team" table - Row added: None - Column added: None

    Charlotte is off until Wednesday. *Schema update*: - Update in "Team" table - Row added: None - Column added: None

  39. [49]

    Team" table - Row added: None - Column added: None ### Final Schema: ```json {

    Charlotte will face the Celtics on Wednesday. *Schema update*: - Update in "Team" table - Row added: None - Column added: None ### Final Schema: ```json { "Team": { "row_headers": ["Hornets", "Knicks"], "column_headers": [ "Wins", "Losses", "Winning streak", "Losing streak", "...

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    - **Update**: - Table: Team - Row: Hornets - Column: Wins - Value: 5

    **Statement**: The Hornets ' record is 5 wins. - **Update**: - Table: Team - Row: Hornets - Column: Wins - Value: 5

  41. [51]

    - **Update**: - Table: Team - Row: Hornets - Column: Losses - Value: 15

    **Statement**: The Hornets ' record is 15 losses. - **Update**: - Table: Team - Row: Hornets - Column: Losses - Value: 15

  42. [52]

    - **Update**: - Table: Team - Row: Hornets - Column: Winning streak - Value: 10

    **Statement**: The Hornets snapped a 10-game winning streak. - **Update**: - Table: Team - Row: Hornets - Column: Winning streak - Value: 10

  43. [53]

    - **Update**: - Table: Team - Row: Knicks - Column: Wins - Value: 4

    **Statement**: The Knicks ' record is 4 wins. - **Update**: - Table: Team - Row: Knicks - Column: Wins - Value: 4

  44. [54]

    - **Update**: - Table: Team - Row: Knicks - Column: Losses - Value: 17

    **Statement**: The Knicks ' record is 17 losses. - **Update**: - Table: Team - Row: Knicks - Column: Losses - Value: 17

  45. [55]

    - **Update**: - Table: Team - Row: Knicks - Column: Losing streak - Value: 7

    **Statement**: The Knicks have a seven-game losing streak. - **Update**: - Table: Team - Row: Knicks - Column: Losing streak - Value: 7

  46. [56]

    - **Update**: - Table: Team - Row: Hornets - Column: Points scored - Value: 103

    **Statement**: The Hornets defeated the Knicks 103-102 on Friday. - **Update**: - Table: Team - Row: Hornets - Column: Points scored - Value: 103

  47. [57]

    - **Update**: - Table: Team - Row: Hornets - Column: Lead entering fourth quarter - Value: 14 22

    **Statement**: Charlotte led by 14 points entering the fourth quarter. - **Update**: - Table: Team - Row: Hornets - Column: Lead entering fourth quarter - Value: 14 22

  48. [58]

    - **Update**: - Table: Team - Row: Knicks - Column: Advantage with 40 seconds - Value: 1

    **Statement**: The Knicks had a one-point advantage with 40 seconds to play. - **Update**: - Table: Team - Row: Knicks - Column: Advantage with 40 seconds - Value: 1

  49. [59]

    - **Update**: - Table: Team - Row: Hornets - Column: Possession with 4 seconds - Value: Yes

    **Statement**: The Hornets had possession of the ball with four seconds to play. - **Update**: - Table: Team - Row: Hornets - Column: Possession with 4 seconds - Value: Yes

  50. [60]

    - **Update**: - Table: Player - Row: Kemba Walker - Column: Buzzer beater - Value: Yes

    **Statement**: Kemba Walker scored at the buzzer. - **Update**: - Table: Player - Row: Kemba Walker - Column: Buzzer beater - Value: Yes

  51. [61]

    - **Update**: - Table: Player - Row: Kemba Walker - Column: Points - Value: 11

    **Statement**: Kemba Walker scored his 11th point of the game. - **Update**: - Table: Player - Row: Kemba Walker - Column: Points - Value: 11

  52. [62]

    - **Update**: - Table: Player - Row: Gerald Henderson - Column: Points - Value: 22

    **Statement**: Gerald Henderson scored 22 points. - **Update**: - Table: Player - Row: Gerald Henderson - Column: Points - Value: 22

  53. [63]

    - **Update**: - Table: Player - Row: Al Jefferson - Column: Points - Value: 16

    **Statement**: Al Jefferson scored 16 points. - **Update**: - Table: Player - Row: Al Jefferson - Column: Points - Value: 16

  54. [64]

    - **Update**: - Table: Player - Row: Al Jefferson - Column: Rebounds - Value: 13

    **Statement**: Al Jefferson had 13 rebounds. - **Update**: - Table: Player - Row: Al Jefferson - Column: Rebounds - Value: 13

  55. [65]

    - **Update**: - Table: Player - Row: Lance Stephenson - Column: Points - Value: 16

    **Statement**: Lance Stephenson scored 16 points. - **Update**: - Table: Player - Row: Lance Stephenson - Column: Points - Value: 16

  56. [66]

    - **Update**: - Table: Player - Row: Lance Stephenson - Column: Field goal attempts - Value: 15

    **Statement**: Lance Stephenson made 7 of 15 field goal attempts. - **Update**: - Table: Player - Row: Lance Stephenson - Column: Field goal attempts - Value: 15

  57. [67]

    - **Update**: - Table: Player - Row: Lance Stephenson - Column: Assists - Value: 6

    **Statement**: Lance Stephenson had six assists. - **Update**: - Table: Player - Row: Lance Stephenson - Column: Assists - Value: 6

  58. [68]

    - **Update**: - Table: Player - Row: Lance Stephenson - Column: Rebounds - Value: 6

    **Statement**: Lance Stephenson had six rebounds. - **Update**: - Table: Player - Row: Lance Stephenson - Column: Rebounds - Value: 6

  59. [69]

    - **Update**: - Table: Player - Row: Gary Neal - Column: Points - Value: 13

    **Statement**: Gary Neal scored 13 points off the bench. - **Update**: - Table: Player - Row: Gary Neal - Column: Points - Value: 13

  60. [70]

    23 - **Update**: - Table: Team - Row: Hornets - Column: Shooting percentage - Value: 48

    **Statement**: The Hornets shot 48 percent from the floor. 23 - **Update**: - Table: Team - Row: Hornets - Column: Shooting percentage - Value: 48

  61. [71]

    - **Update**: - Table: Team - Row: Hornets - Column: Three-point percentage - Value: 22

    **Statement**: The Hornets made 22 percent of their three-point attempts. - **Update**: - Table: Team - Row: Hornets - Column: Three-point percentage - Value: 22

  62. [72]

    - **Update**: - Table: Team - Row: Hornets - Column: Three-point attempts - Value: 18

    **Statement**: The Hornets made 4 of 18 three-point attempts. - **Update**: - Table: Team - Row: Hornets - Column: Three-point attempts - Value: 18

  63. [73]

    - **Update**: - Table: Player - Row: Carmelo Anthony - Column: Points - Value: 32

    **Statement**: Carmelo Anthony scored 32 points. - **Update**: - Table: Player - Row: Carmelo Anthony - Column: Points - Value: 32

  64. [74]

    - **Update**: - Table: Player - Row: Carmelo Anthony - Column: Rebounds - Value: 7

    **Statement**: Carmelo Anthony had seven rebounds. - **Update**: - Table: Player - Row: Carmelo Anthony - Column: Rebounds - Value: 7

  65. [75]

    - **Update**: - Table: Player - Row: Carmelo Anthony - Column: Assists - Value: 4

    **Statement**: Carmelo Anthony had four assists. - **Update**: - Table: Player - Row: Carmelo Anthony - Column: Assists - Value: 4

  66. [76]

    scored 13 points

    **Statement**: Tim Hardaway, Jr. scored 13 points. - **Update**: - Table: Player - Row: Tim Hardaway, Jr. - Column: Points - Value: 13

  67. [77]

    Smith scored 14 points

    **Statement**: J.R. Smith scored 14 points. - **Update**: - Table: Player - Row: J.R. Smith - Column: Points - Value: 14

  68. [78]

    Smith had five assists

    **Statement**: J.R. Smith had five assists. - **Update**: - Table: Player - Row: J.R. Smith - Column: Assists - Value: 5

  69. [79]

    Smith had four rebounds

    **Statement**: J.R. Smith had four rebounds. - **Update**: - Table: Player - Row: J.R. Smith - Column: Rebounds - Value: 4

  70. [80]

    - **Update**: - Table: Team - Row: Knicks - Column: Rebounds - Value: 34

    **Statement**: The Knicks were out-rebounded 41 to 34. - **Update**: - Table: Team - Row: Knicks - Column: Rebounds - Value: 34

  71. [81]

    - **Update**: - Table: Team - Row: Knicks - Column: Turnovers - Value: 6

    **Statement**: The Knicks committed six turnovers. - **Update**: - Table: Team - Row: Knicks - Column: Turnovers - Value: 6

  72. [82]

    - **Update**: 24 - Table: Player - Row: Samuel Dalembert - Column: Turnovers - Value: 3

    **Statement**: Samuel Dalembert committed three turnovers. - **Update**: 24 - Table: Player - Row: Samuel Dalembert - Column: Turnovers - Value: 3

  73. [83]

    - **Update**: - Table: Team - Row: Knicks - Column: Next game - Value: Blazers on Sunday

    **Statement**: The Knicks will host the Blazers on Sunday. - **Update**: - Table: Team - Row: Knicks - Column: Next game - Value: Blazers on Sunday

  74. [84]

    Football Match

    **Statement**: Charlotte is off until Wednesday. - **Update**: - Table: Team - Row: Hornets - Column: Next game - Value: Celtics on Wednesday ### Final Output Tables: ### Team | Team | Wins | Losses | Winning streak | Losing streak | Points scored | Lead entering fourth quarte...

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    Home Team

    (Kevin Durant, Season-High, 44 points) Provide tuples for the given table in the following output: T3 Tuple Integrate Instruction: According to the live text, please count the number of events of both teams. 30 Note that goals and saved attempts and blocked attempts and missed...

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    (Oklahoma City Thunder, Record, 16-17)

  77. [98]

    (Phoenix Suns, Record, 18-16)

  78. [99]

    (Oklahoma City Thunder, Points Scored, 137)

  79. [100]

    (Phoenix Suns, Points Scored, 134)

  80. [101]

    (Oklahoma City Thunder, Game Outcome, Win)

  81. [102]

    (Phoenix Suns, Game Outcome, Loss)

  82. [103]

    (Oklahoma City Thunder, Recent Performance, Won 3 of last 4 games)

  83. [104]

    (Kevin Durant, Injury, Ankle Sprain)

  84. [105]

    (Kevin Durant, Games Missed, 6)

  85. [106]

    (Kevin Durant, Points Scored, 44)

  86. [107]

    (Kevin Durant, Minutes Played, 40)

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    | | <Row Header 1> | <Cell Value for (Row Header 1, Column Header 1)> |

    (Kevin Durant, Season-High, 44 points) Output: ### Team | Team | Wins | Losses | Total Points | | Thunder | 16 | 17| None | | Suns | 18 | None | 134 | ### Player | Player | Points | Minutes Played | | Kevin Durant | 44 | 40 | **Output Format**: ### Team | Team | <Column Header...

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    Please focus on the table format and extract all relevant tuples in (team or player name, attribute, value) format:

    Extract team and player information from the following input. Please focus on the table format and extract all relevant tuples in (team or player name, attribute, value) format:

  89. [110]

    <Table name>

    Integrate these tuples into two tables in the output format given below. Illustration : Input: The Oklahoma City Thunder (16 - 17) defeated the Phoenix Suns (18 - 16) 137 - 134 in overtime on Wednesday. Oklahoma City has won three of their last four games. Kevin Durant returne...

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    Team" table - Row added:

    The Oklahoma City Thunder 's record is 16 wins. *Schema update*: - Update in "Team" table - Row added: "Thunder" - Column added: "Wins" ... ### Final Schema: { "Team": { "row_headers": ["Thunder", "Suns"], "column_headers": ["Wins", "Losses", "Total points"] } "Player": { "row...

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    As shown in the illustration above, for *every given statement* return the updates done to the schema and generate the Team Table and Player Table schema.,→

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    Do not return schema directly in any case

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    Team": {

    Provide the *OUTPUT* with *REASONING STEPS* in the specified format only. E.4.3 M&M Table Generation M&M Table Generation for Rotowire You are an expert in converting unstructured text into structured tables. Your task is to process a series of atomic statements and update a s...

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    Handle Missing Data: If a column value is not present in the statements, keep it as None

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    Ensure uniformity in the format of data across the table

    Structural Integrity: Do not add or remove any rows or columns unless explicitly instructed by the data. Ensure uniformity in the format of data across the table

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    Table formatting: Use "|" to separate cells Provide the *OUTPUT* with *REASONING STEPS* in the specified format only. 38

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    In Proceedings of the 18th An- nual SIGdial Meeting on Discourse and Dialogue , pages 201–206, Saarbrücken, Germany

    The E2E dataset: New challenges for end- to-end generation. In Proceedings of the 18th An- nual SIGdial Meeting on Discourse and Dialogue , pages 201–206, Saarbrücken, Germany. Association for Computational Linguistics. Nilay Patel, Shivashankar Subramanian, Siddhant Garg, Pra...

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    In Proceedings of the 62nd Annual Meet- ing of the Association for Computational Linguis- tics (Volume 1: Long Papers) , pages 7876–7896, Bangkok, Thailand

    STRUCTSUM generation for faster text com- prehension. In Proceedings of the 62nd Annual Meet- ing of the Association for Computational Linguis- tics (Volume 1: Long Papers) , pages 7876–7896, Bangkok, Thailand. Association for Computational Linguistics. 10 Tushar Khot, Harsh T...

    2022 arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.