@inproceedings{zhang-etal-2024-opent2t,
title = "{O}pen{T}2{T}: An Open-Source Toolkit for Table-to-Text Generation",
author = "Zhang, Haowei and
Si, Shengyun and
Zhao, Yilun and
Xie, Lujing and
Xu, Zhijian and
Chen, Lyuhao and
Nan, Linyong and
Wang, Pengcheng and
Tang, Xiangru and
Cohan, Arman",
editor = "Hernandez Farias, Delia Irazu and
Hope, Tom and
Li, Manling",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-demo.27",
pages = "259--269",
abstract = "Table data is pervasive in various industries, and its comprehension and manipulation demand significant time and effort for users seeking to extract relevant information. Consequently, an increasing number of studies have been directed towards table-to-text generation tasks. However, most existing methods are benchmarked solely on a limited number of datasets with varying configurations, leading to a lack of unified, standardized, fair, and comprehensive comparison between methods. This paper presents OpenT2T, the first open-source toolkit for table-to-text generation, designed to reproduce existing large language models (LLMs) for performance comparison and expedite the development of new models.We have implemented and compared a wide range of LLMs under zero- and few-shot settings on 9 table-to-text generation datasets, covering data insight generation, table summarization, and free-form table question answering. Additionally, we maintain a public leaderboard to provide insights for future work into how to choose appropriate table-to-text generation systems for real-world scenarios.",
}
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<abstract>Table data is pervasive in various industries, and its comprehension and manipulation demand significant time and effort for users seeking to extract relevant information. Consequently, an increasing number of studies have been directed towards table-to-text generation tasks. However, most existing methods are benchmarked solely on a limited number of datasets with varying configurations, leading to a lack of unified, standardized, fair, and comprehensive comparison between methods. This paper presents OpenT2T, the first open-source toolkit for table-to-text generation, designed to reproduce existing large language models (LLMs) for performance comparison and expedite the development of new models.We have implemented and compared a wide range of LLMs under zero- and few-shot settings on 9 table-to-text generation datasets, covering data insight generation, table summarization, and free-form table question answering. Additionally, we maintain a public leaderboard to provide insights for future work into how to choose appropriate table-to-text generation systems for real-world scenarios.</abstract>
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%0 Conference Proceedings
%T OpenT2T: An Open-Source Toolkit for Table-to-Text Generation
%A Zhang, Haowei
%A Si, Shengyun
%A Zhao, Yilun
%A Xie, Lujing
%A Xu, Zhijian
%A Chen, Lyuhao
%A Nan, Linyong
%A Wang, Pengcheng
%A Tang, Xiangru
%A Cohan, Arman
%Y Hernandez Farias, Delia Irazu
%Y Hope, Tom
%Y Li, Manling
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F zhang-etal-2024-opent2t
%X Table data is pervasive in various industries, and its comprehension and manipulation demand significant time and effort for users seeking to extract relevant information. Consequently, an increasing number of studies have been directed towards table-to-text generation tasks. However, most existing methods are benchmarked solely on a limited number of datasets with varying configurations, leading to a lack of unified, standardized, fair, and comprehensive comparison between methods. This paper presents OpenT2T, the first open-source toolkit for table-to-text generation, designed to reproduce existing large language models (LLMs) for performance comparison and expedite the development of new models.We have implemented and compared a wide range of LLMs under zero- and few-shot settings on 9 table-to-text generation datasets, covering data insight generation, table summarization, and free-form table question answering. Additionally, we maintain a public leaderboard to provide insights for future work into how to choose appropriate table-to-text generation systems for real-world scenarios.
%U https://aclanthology.org/2024.emnlp-demo.27
%P 259-269
Markdown (Informal)
[OpenT2T: An Open-Source Toolkit for Table-to-Text Generation](https://aclanthology.org/2024.emnlp-demo.27) (Zhang et al., EMNLP 2024)
ACL
- Haowei Zhang, Shengyun Si, Yilun Zhao, Lujing Xie, Zhijian Xu, Lyuhao Chen, Linyong Nan, Pengcheng Wang, Xiangru Tang, and Arman Cohan. 2024. OpenT2T: An Open-Source Toolkit for Table-to-Text Generation. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 259–269, Miami, Florida, USA. Association for Computational Linguistics.