Towards Table-to-Text Generation with Numerical Reasoning

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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BLEU: a method for automatic evaluation of machine translation

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BERTScore: Evaluating Text Generation with BERT

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Neural Text Generation from Structured Data with Application to the Biography Domain

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Challenges in Data-to-Document Generation

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Compositional Semantic Parsing on Semi-Structured Tables

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Learning semantic correspondences with less supervision

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ToTTo: A Controlled Table-To-Text Generation Dataset

Parikh, A., Wang, X., Gehrmann, S., Faruqui, M., Dhingra, B., Yang, D., & Das, D. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.89 · Cited by 252 · see citing works

TabFact: A Large-scale Dataset for Table-based Fact Verification

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Handling Divergent Reference Texts when Evaluating Table-to-Text Generation

Dhingra, B., Faruqui, M., Parikh, A., Chang, M.-W., Das, D., & Cohen, W. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1483 · Cited by 150 · see citing works

Text-to-Text Pre-Training for Data-to-Text Tasks

Kale, M., & Rastogi, A. (2020) · International Conference on Natural Language Generation · doi:10.18653/v1/2020.inlg-1.14 · Cited by 145 · see citing works

Logical Natural Language Generation from Open-Domain Tables

Chen, W., Chen, J., Su, Y., Chen, Z., & Wang, W. Y. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.708 · Cited by 130 · see citing works

Few-Shot NLG with Pre-Trained Language Model

Chen, Z., Eavani, H., Chen, W., Liu, Y., & Wang, W. Y. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.18 · Cited by 127 · see citing works

Data-to-text Generation with Entity Modeling

Puduppully, R., Dong, L., & Lapata, M. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1195 · Cited by 116 · see citing works

INFOTABS: Inference on Tables as Semi-structured Data

Gupta, V., Mehta, M., Nokhiz, P., & Srikumar, V. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.210 · Cited by 81 · see citing works

Learning to Generate Market Comments from Stock Prices

Murakami, S., Watanabe, A., Miyazawa, A., Goshima, K., Yanase, T., Takamura, H., & Miyao, Y. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1126 · Cited by 59 · see citing works

Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints

Wang, Z., Wang, X., An, B., Yu, D., & Chen, C. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.101 · Cited by 59 · see citing works

Template Guided Text Generation for Task-Oriented Dialogue

Kale, M., & Rastogi, A. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.527 · Cited by 58 · see citing works

Operation-guided Neural Networks for High Fidelity Data-To-Text Generation

Nie, F., Wang, J., Yao, J.-G., Pan, R., & Lin, C.-Y. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1422 · Cited by 46 · see citing works

Text-to-Text Pre-Training for Data-to-Text TasksPreprint

Kale, M., & Rastogi, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.10433 · Cited by 30 · see citing works

A Neural Question Answering Model Based on Semi-Structured Tables

Wang, H., Zhang, X., Ma, S., Sun, X., Wang, H., & Wang, M. (2018) · International Conference on Computational Linguistics · Cited by 5 · see citing works

Metric-Type Identification for Multi-Level Header Numerical Tables in Scientific Papers

Suadaa, L. H., Kamigaito, H., Okumura, M., & Takamura, H. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.267 · Cited by 2 · see citing works

Survey of Hallucination in Natural Language Generation

Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023) · ACM Computing Surveys · doi:10.1145/3571730 · Cited by 5,025 · see citing works

Transformers for Tabular Data Representation: A Survey of Models and Applications

Badaro, G., Saeed, M., & Papotti, P. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00544 · Cited by 96 · see citing works

A Systematic Literature Review on Text Generation Using Deep Neural Network Models

Fatima, N., Imran, A. S., Kastrati, Z., Daudpota, S. M., & Abdullah, A. (2022) · IEEE Access · doi:10.1109/access.2022.3174108 · Cited by 65 · see citing works

HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation

Cheng, Z., Dong, H., Wang, Z., Jia, R., Guo, J., Gao, Y., Han, S., Lou, J., & Zhang, D. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.78 · Cited by 63 · see citing works

MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data

Zhao, Y., Li, Y., Li, C., & Zhang, R. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.454 · Cited by 71 · see citing works

PACIFIC: Towards Proactive Conversational Question Answering over Tabular and Textual Data in Finance

Deng, Y., Lei, W., Zhang, W., Lam, W. P., & Chua, T. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.469 · Cited by 37 · see citing works

GPT-Based Models Meet Simulation: How to Efficiently use Large-Scale Pre-Trained Language Models Across Simulation Tasks

Giabbanelli, P. J. (2023) · Winter Simulation Conference · doi:10.1109/wsc60868.2023.10408017 · Cited by 30 · see citing works

A Survey on Neural Data-to-Text Generation

Lin, Y., Ruan, T., Liu, J., & Wang, H. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3304385 · Cited by 22 · see citing works

FORTAP: Using Formulas for Numerical-Reasoning-Aware Table Pretraining

Cheng, Z., Dong, H., Jia, R., Wu, P., Han, S., Cheng, F., & Zhang, D. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.82 · Cited by 26 · see citing works

TaCube: Pre-computing Data Cubes for Answering Numerical-Reasoning Questions over Tabular Data

Zhou, F., Hu, M., Dong, H., Cheng, Z., Cheng, F., Shi, H., & Zhang, D. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.145 · Cited by 19 · see citing works

PLOG: Table-to-Logic Pretraining for Logical Table-to-Text Generation

Liu, A., Dong, H., Okazaki, N., Shi, H., & Zhang, D. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.373 · Cited by 18 · see citing works

Towards Table-to-Text Generation with Pretrained Language Model: A Table Structure Understanding and Text Deliberating Approach

Chen, M., Lu, X., Xu, T., Li, Y., Jingbo, Z., Dou, D., & Xiong, H. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.562 · Cited by 16 · see citing works

PubHealthTab: A Public Health Table-based Dataset for Evidence-based Fact Checking

Akhtar, M., Cocarascu, O., & Simperl, E. P. B. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.1 · Cited by 12 · see citing works

SciCap+: A Knowledge Augmented Dataset to Study the Challenges of Scientific Figure Captioning

Yang, Z., Dabre, R., Tanaka, H., & Okazaki, N. (2024) · Journal of Natural Language Processing · doi:10.5715/jnlp.31.1140 · Cited by 12 · see citing works

Natural Language Explanations for Machine Learning Classification Decisions

Burton, J., Moubayed, N. A., & Enshaei, A. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191637 · Cited by 7 · see citing works

QTSumm: Query-Focused Summarization over Tabular Data

Zhao, Y., Qi, Z., Nan, L., Mi, B., Liu, Y., Zou, W., Han, S., Chen, R., Tang, X., Xu, Y., et al. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.74 · Cited by 10 · see citing works

MedT2T: An adaptive pointer constrain generating method for a new medical text-to-table task

Zhao, W., Gu, D., Yang, X., Jia, M., Liang, C., Wang, X., & Золотарев, О. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.07.030 · Cited by 7 · see citing works

Scientific document processing: challenges for modern learning methods

Kashyap, A. R., Yang, Y., & Kan, M. (2023) · International Journal on Digital Libraries · doi:10.1007/s00799-023-00352-7 · Cited by 9 · see citing works

Table Caption Generation in Scholarly Documents Leveraging Pre-trained Language Models

Xu, J. H., Shinden, K., & Kato, M. P. (2021) · IEEE Global Conference on Consumer Electronics (GCCE) · doi:10.1109/gcce53005.2021.9621890 · Cited by 7 · see citing works

TaTA: A Multilingual Table-to-Text Dataset for African Languages

Gehrmann, S., Ruder, S., Nikolaev, V. A., Botha, J., Chavinda, M., Parikh, A. P., & Rivera, C. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.118 · Cited by 7 · see citing works

Neural Methods for Data-to-text Generation

Sharma, M., Gogineni, A. K., & Ramakrishnan, N. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3660639 · Cited by 17 · see citing works

UniRaG: Unification, Retrieval, and Generation for Multimodal Question Answering With Pre-Trained Language Models

Lim, Q. Z., Lee, C. P., Lim, K. M., & Samingan, A. K. (2024) · IEEE Access · doi:10.1109/access.2024.3403101 · Cited by 6 · see citing works

TableNarrator: Making Image Tables Accessible to Blind and Low Vision People

Mo, Y., Huang, G., Li, L., Deng, D., Yu, Z., Xu, Y., Ye, K., Zhou, S., & Bu, J. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714329 · Cited by 6 · see citing works

Turkish Data-to-Text Generation Using Sequence-to-Sequence Neural Networks

Demir, Ş. (2022) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3543826 · Cited by 6 · see citing works

TabGenie: A Toolkit for Table-to-Text Generation

Kasner, Z., Garanina, E., Plátek, O., & Dušek, O. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-demo.42 · Cited by 4 · see citing works

Quality Control for Distantly-Supervised Data-to-Text Generation via Meta Learning

Gong, H., Feng, X., & Qin, B. (2023) · Applied Sciences · doi:10.3390/app13095573 · Cited by 4 · see citing works

Leveraging Large Language Models for Flexible and Robust Table-to-Text Generation

Oro, E., Grandis, L. D., Granata, F. M., & Ruffolo, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-68309-1_19 · Cited by 3 · see citing works

Enhancing neural network predictions with finetuned numeric embeddings for stock trend forecasting

Trivedi, A., & Sangeetha, S. (2025) · Soft Computing · doi:10.1007/s00500-025-10483-5 · Cited by 3 · see citing works

Improving Cross-task Generalization of Unified Table-to-text Models with Compositional Task Configurations

Chen, J., Zhang, Y., Liu, L., Dong, R., Chen, X., Ng, P., Wang, W. Y., & Huang, Z. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.341 · Cited by 3 · see citing works

Improving compositional generalization for multi-step quantitative reasoning in question answering

Nourbakhsh, A., Jiao, C., Shah, S., & Rosé, C. P. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.125 · Cited by 3 · see citing works

A Table-to-Text Framework with Heterogeneous Multidominance Attention and Self-Evaluated Multi-Pass Deliberation

Chen, X., Lu, X., Xin, H., Peng, W., Duan, H., Jiang, F., Zhou, J., & Xiong, H. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.44 · Cited by 3 · see citing works

ReTAG: Reasoning Aware Table to Analytic Text Generation

Ghosal, D., Nema, P., & Raghuveer, A. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.389 · Cited by 4 · see citing works

Artificial intelligence for science and engineering: A priority for public investment in research and development

Hey, T. (2023) · OECD eBooks · doi:10.1787/7b7b1bce-en · Cited by 2 · see citing works

Towards Feasible Counterfactual Explanations: A Taxonomy Guided Template-Based NLG Method

Salimi, P., Wiratunga, N., Corsar, D., & Wijekoon, A. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230499 · Cited by 2 · see citing works

FERMAT: An Alternative to Accuracy for Numerical Reasoning

Sivakumar, J., & Moosavi, N. S. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.838 · Cited by 2 · see citing works

Arithmetic-Based Pretraining Improving Numeracy of Pretrained Language Models

Petrak, D., Moosavi, N. S., & Gurevych, I. (2023) · Joint Conference on Lexical and Computational Semantics · doi:10.18653/v1/2023.starsem-1.42 · Cited by 2 · see citing works

Towards Controlled Table-to-Text Generation with Scientific Reasoning

Guo, Z., Zhou, J., Qi, J., Yan, M., He, Z., Zheng, G., Lin, Z., Wang, X., & Zhou, C. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446479 · Cited by 3 · see citing works

Caption Alignment and Structure-Aware Attention for Scientific Table-to-Text Generation

Wu, J., Karlsson, B. F., & Okumura, M. (2024) · IEEE Access · doi:10.1109/access.2024.3507772 · Cited by 1 · see citing works

Enhancing Free-Form Table Question Answering Models by Distilling Relevant-Cell-Based Rationales

Yang, Z., Wang, S., Yan, Y., Liu, P., & Yu, D. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-8367-0_1 · Cited by 2 · see citing works

TabVer: Tabular Fact Verification with Natural Logic

Aly, R., & Vlachos, A. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00722 · Cited by 2 · see citing works

Lessons from shortcomings in machine learning for medical imaging

Varoquaux, G. P., & Cheplygina, V. (2023) · OECD eBooks · doi:10.1787/b885eecd-en · Cited by 2 · see citing works

A Multi-Task Learning Framework for Reading Comprehension of Scientific Tabular Data

Xu, Y., Zhang, M., Fan, J., Luo, Z., & Yang, Y. (2024) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde60146.2024.00285 · Cited by 1 · see citing works

MSIF: Multi-source Information Fusion for Financial Question Answering

Lin, M., Zeng, D., Ouyang, J., & Shen, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72356-8_26 · Cited by 1 · see citing works

High-performance computing leadership to enable advances in artificial intelligence and a thriving compute ecosystem

Tourassi, G. D., Shankar, M. A., & Wang, F. (2023) · OECD eBooks · doi:10.1787/0e1b4c2f-en

Eroom’s Law and the decline in the productivity of biopharmaceutical R&D

Scannell, J. W. (2023) · OECD eBooks · doi:10.1787/6f256d17-en

The end of Moore’s Law? Innovation in computer systems continues at a high pace

Kressel, H. (2023) · OECD eBooks · doi:10.1787/63e48242-en · Cited by 1 · see citing works

Quantifying the “cognitive extent” of science and how it has changed over time and across countries

Milojević, S. (2023) · OECD eBooks · doi:10.1787/1f717652-en

Interpretability: Should – and can – we understand the reasoning of machine-learning systems?

Cartwright, H. (2023) · OECD eBooks · doi:10.1787/2d0478b9-en

Preface

OECD (2023) · OECD eBooks · doi:10.1787/f1262928-en

An Inclusive Notion of Text

Kuznetsov, I., & Gurevych, I. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.633

Arxiv Tables: Document Understanding Challenge Linking Texts and Tables

Konopka, K., Turski, M., & Graliński, F. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-41501-2_7

Parameterization before Meta-Analysis: Cross-Modal Embedding Clustering for Forest Ecology Question-Answering

Rui, T., Zhu, M., Cao, H., & Ren, H. (2024) · Forests · doi:10.3390/f15091670

CoT-Planner: Chain-of-Thoughts as the Content Planner for Few-shot Table-to-Text Generation Reduces the Hallucinations from LLMs

Lin, Y., Bian, Y., Yu, G., Xue, D., Lu, W., Liu, J., & Ruan, T. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228570

FORTAP: Using Formulas for Numerical-Reasoning-Aware Table Pretraining

Cheng, Z., Dong, H., Jia, R., Wu, P., Han, S., Cheng, F., & Zhang, D. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.82 · Cited by 26 · see citing works

Enhancing Financial Table and Text Question Answering with Tabular Graph and Numerical Reasoning

Nararatwong, R., Kertkeidkachorn, N., & Ichise, R. (2022) · Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the International Joint Conference on Natural Language Processing (AACL-IJCNLP) · doi:10.18653/v1/2022.aacl-main.72 · Cited by 7 · see citing works

Logical Table-to-Text Generation: Challenges, Methods, and Reasoning

Trigg, L., & Hougen, D. F. (2025) · International Joint Conference on Natural Language Processing and Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL) · doi:10.18653/v1/2025.findings-ijcnlp.103

NumNet: Machine Reading Comprehension with Numerical Reasoning

Ran, Q., Lin, Y., Li, P., Zhou, J., & Liu, Z. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1251 · Cited by 108 · see citing works

Learning number reasoning for numerical table-to-text generation

Feng, X., Gong, H., Chen, Y., Sun, Y., Qin, B., Bi, W., Liu, X., & Liu, T. (2021) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-021-01305-9 · Cited by 1 · see citing works