{"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/an-integrated-approach-for-keyphrase","title":"An Integrated Approach for Keyphrase Generation via Exploring the Power of Retrieval and Extraction","arxiv_id":"1904.03454","date":"2019-04-06","proceeding":"NAACL 2019 6","authors":["Wang Chen","Hou Pong Chan","Piji Li","Lidong Bing","Irwin King"],"abstract":"In this paper, we present a novel integrated approach for keyphrase\ngeneration (KG). Unlike previous works which are purely extractive or\ngenerative, we first propose a new multi-task learning framework that jointly\nlearns an extractive model and a generative model. Besides extracting\nkeyphrases, the output of the extractive model is also employed to rectify the\ncopy probability distribution of the generative model, such that the generative\nmodel can better identify important contents from the given document. Moreover,\nwe retrieve similar documents with the given document from training data and\nuse their associated keyphrases as external knowledge for the generative model\nto produce more accurate keyphrases. For further exploiting the power of\nextraction and retrieval, we propose a neural-based merging module to combine\nand re-rank the predicted keyphrases from the enhanced generative model, the\nextractive model, and the retrieved keyphrases. Experiments on the five KG\nbenchmarks demonstrate that our integrated approach outperforms the\nstate-of-the-art methods.","url_abs":"http://arxiv.org/abs/1904.03454v1","url_pdf":"http://arxiv.org/pdf/1904.03454v1.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":"an-integrated-approach-for-keyphrase","repo_url":"https://github.com/Chen-Wang-CUHK/KG-KE-KR-M","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"keyphrase-generation","task_name":"Keyphrase Generation"},{"task_slug":"multi-task-learning","task_name":"Multi-Task Learning"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.03454","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}