Towards Controlled Table-to-Text Generation with Scientific Reasoning

• URL: http://arxiv.org/abs/2312.05402v1
• Date: Fri, 8 Dec 2023 22:57:35 GMT
• Title: Towards Controlled Table-to-Text Generation with Scientific Reasoning
• Authors: Zhixin Guo, Jianping Zhou, Jiexing Qi, Mingxuan Yan, Ziwei He, Guanjie Zheng, Zhouhan Lin, Xinbing Wang, Chenghu Zhou
• Abstract summary: We present a new task for generating fluent and logical descriptions that match user preferences over scientific data, aiming to automate scientific document analysis. We construct a new challenging dataset,SciTab, consisting of table-description pairs extracted from the scientific literature, with highlighted cells and corresponding domain-specific knowledge base. The results showed that large models struggle to produce accurate content that aligns with user preferences. As the first of its kind, our work should motivate further research in scientific domains.
• Score: 46.87189607486007
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The sheer volume of scientific experimental results and complex technical statements, often presented in tabular formats, presents a formidable barrier to individuals acquiring preferred information. The realms of scientific reasoning and content generation that adhere to user preferences encounter distinct challenges. In this work, we present a new task for generating fluent and logical descriptions that match user preferences over scientific tabular data, aiming to automate scientific document analysis. To facilitate research in this direction, we construct a new challenging dataset CTRLSciTab consisting of table-description pairs extracted from the scientific literature, with highlighted cells and corresponding domain-specific knowledge base. We evaluated popular pre-trained language models to establish a baseline and proposed a novel architecture outperforming competing approaches. The results showed that large models struggle to produce accurate content that aligns with user preferences. As the first of its kind, our work should motivate further research in scientific domains.

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