{"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/data-to-text-generation-with-content","title":"Data-to-Text Generation with Content Selection and Planning","arxiv_id":"1809.00582","date":"2018-09-03","proceeding":null,"authors":["Ratish Puduppully","Li Dong","Mirella Lapata"],"abstract":"Recent advances in data-to-text generation have led to the use of large-scale\ndatasets and neural network models which are trained end-to-end, without\nexplicitly modeling what to say and in what order. In this work, we present a\nneural network architecture which incorporates content selection and planning\nwithout sacrificing end-to-end training. We decompose the generation task into\ntwo stages. Given a corpus of data records (paired with descriptive documents),\nwe first generate a content plan highlighting which information should be\nmentioned and in which order and then generate the document while taking the\ncontent plan into account. Automatic and human-based evaluation experiments\nshow that our model outperforms strong baselines improving the state-of-the-art\non the recently released RotoWire dataset.","url_abs":"http://arxiv.org/abs/1809.00582v2","url_pdf":"http://arxiv.org/pdf/1809.00582v2.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":"data-to-text-generation-with-content","repo_url":"https://github.com/ratishsp/data2text-plan-py","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"data-to-text-generation-with-content","repo_url":"https://github.com/jugalw13/Red-Hat-Hack","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"data-to-text-generation","task_name":"Data-to-Text Generation"},{"task_slug":"descriptive","task_name":"Descriptive"},{"task_slug":"text-generation","task_name":"Text Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/data-to-text-generation-on-rotowire","task":"Data-to-Text Generation","dataset":"RotoWire","model":"Neural Content Planning + conditional copy","rank_in_archive_order":4,"of":6,"metrics":{"BLEU":"16.50"},"uses_additional_data":false},{"leaderboard":"/sota/data-to-text-generation-on-rotowire-content","task":"Data-to-Text Generation","dataset":"RotoWire (Content Ordering)","model":"Neural Content Planning + conditional copy","rank_in_archive_order":2,"of":5,"metrics":{"BLEU":"16.50","DLD":"18.58%"},"uses_additional_data":false},{"leaderboard":"/sota/data-to-text-generation-on-rotowire-relation","task":"Data-to-Text Generation","dataset":"RotoWire (Relation Generation)","model":"Neural Content Planning + conditional copy","rank_in_archive_order":5,"of":6,"metrics":{"Precision":"87.47%","count":"34.28"},"uses_additional_data":false},{"leaderboard":"/sota/data-to-text-generation-on-rotowire-content-1","task":"Data-to-Text Generation","dataset":"Rotowire (Content Selection)","model":"Neural Content Planning + conditional copy","rank_in_archive_order":3,"of":5,"metrics":{"Precision":"34.18%","Recall":"51.22%"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.00582","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}