{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/enhancing-content-planning-for-table-to-text","title":"Enhancing Content Planning for Table-to-Text Generation with Data Understanding and Verification","arxiv_id":null,"date":"2020-11-01","proceeding":"Findings of the Association for Computational Linguistics 2020","authors":["Heng Gong","Wei Bi","Xiaocheng Feng","Bing Qin","Xiaojiang Liu","Ting Liu"],"abstract":"Neural table-to-text models, which select and order salient data, as well as verbalizing them fluently via surface realization, have achieved promising progress. Based on results from previous work, the performance bottleneck of current models lies in the stage of content planing (selecting and ordering salient content from the input). That is, performance drops drastically when an oracle content plan is replaced by a model-inferred one during surface realization. In this paper, we propose to enhance neural content planning by (1) understanding data values with contextual numerical value representations that bring the sense of value comparison into content planning; (2) verifying the importance and ordering of the selected sequence of records with policy gradient. We evaluated our model on ROTOWIRE and MLB, two datasets on this task, and results show that our model outperforms existing systems with respect to content planning metrics.","url_abs":"https://aclanthology.org/2020.findings-emnlp.262","url_pdf":"https://aclanthology.org/2020.findings-emnlp.262.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"enhancing-content-planning-for-table-to-text","repo_url":"https://github.com/ErnestGong/data2text-duv","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"table-to-text-generation","task_name":"Table-to-Text Generation"},{"task_slug":"text-generation","task_name":"Text Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}