{"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/imbalanced-multi-label-classification-using","title":"Imbalanced multi-label classification using multi-task learning with extractive summarization","arxiv_id":"1903.06963","date":"2019-03-16","proceeding":null,"authors":["John Brandt"],"abstract":"Extractive summarization and imbalanced multi-label classification often\nrequire vast amounts of training data to avoid overfitting. In situations where\ntraining data is expensive to generate, leveraging information between tasks is\nan attractive approach to increasing the amount of available information. This\npaper employs multi-task training of an extractive summarizer and an RNN-based\nclassifier to improve summarization and classification accuracy by 50% and 75%,\nrespectively, relative to RNN baselines. We hypothesize that concatenating\nsentence encodings based on document and class context increases\ngeneralizability for highly variable corpuses.","url_abs":"http://arxiv.org/abs/1903.06963v1","url_pdf":"http://arxiv.org/pdf/1903.06963v1.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":"imbalanced-multi-label-classification-using","repo_url":"https://github.com/JohnMBrandt/text-classification","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"extractive-summarization","task_name":"Extractive Summarization"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"multi-label-classification-2","task_name":"MUlTI-LABEL-ClASSIFICATION"},{"task_slug":"multi-label-classification","task_name":"Multi-Label Classification"},{"task_slug":"multi-task-learning","task_name":"Multi-Task Learning"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}