{"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/knowledge-graph-empowered-entity-description","title":"ENT-DESC: Entity Description Generation by Exploring Knowledge Graph","arxiv_id":"2004.14813","date":"2020-04-30","proceeding":"EMNLP 2020 11","authors":["Liying Cheng","Dekun Wu","Lidong Bing","Yan Zhang","Zhanming Jie","Wei Lu","Luo Si"],"abstract":"Previous works on knowledge-to-text generation take as input a few RDF triples or key-value pairs conveying the knowledge of some entities to generate a natural language description. Existing datasets, such as WIKIBIO, WebNLG, and E2E, basically have a good alignment between an input triple/pair set and its output text. However, in practice, the input knowledge could be more than enough, since the output description may only cover the most significant knowledge. In this paper, we introduce a large-scale and challenging dataset to facilitate the study of such a practical scenario in KG-to-text. Our dataset involves retrieving abundant knowledge of various types of main entities from a large knowledge graph (KG), which makes the current graph-to-sequence models severely suffer from the problems of information loss and parameter explosion while generating the descriptions. We address these challenges by proposing a multi-graph structure that is able to represent the original graph information more comprehensively. Furthermore, we also incorporate aggregation methods that learn to extract the rich graph information. Extensive experiments demonstrate the effectiveness of our model architecture.","url_abs":"https://arxiv.org/abs/2004.14813v2","url_pdf":"https://arxiv.org/pdf/2004.14813v2.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":"knowledge-graph-empowered-entity-description","repo_url":"https://github.com/LiyingCheng95/EntityDescriptionGeneration","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"mxnet","reach":null}],"tasks":[{"task_slug":"graph-to-sequence","task_name":"Graph-to-Sequence"},{"task_slug":"kg-to-text","task_name":"KG-to-Text Generation"},{"task_slug":"knowledge-graphs","task_name":"Knowledge Graphs"},{"task_slug":"text-generation","task_name":"Text Generation"}],"methods":[],"datasets_introduced":[{"slug":"ent-desc","name":"ENT-DESC","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/kg-to-text-generation-on-ent-desc","task":"KG-to-Text Generation","dataset":"ENT-DESC","model":"MGCN+sum","rank_in_archive_order":1,"of":1,"metrics":{"BLEU":"26.4"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2004.14813","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}