{"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/autoencoder-as-assistant-supervisor-improving","title":"Autoencoder as Assistant Supervisor: Improving Text Representation for Chinese Social Media Text Summarization","arxiv_id":"1805.04869","date":"2018-05-13","proceeding":"ACL 2018 7","authors":["Shuming Ma","Xu sun","Junyang Lin","Houfeng Wang"],"abstract":"Most of the current abstractive text summarization models are based on the\nsequence-to-sequence model (Seq2Seq). The source content of social media is\nlong and noisy, so it is difficult for Seq2Seq to learn an accurate semantic\nrepresentation. Compared with the source content, the annotated summary is\nshort and well written. Moreover, it shares the same meaning as the source\ncontent. In this work, we supervise the learning of the representation of the\nsource content with that of the summary. In implementation, we regard a summary\nautoencoder as an assistant supervisor of Seq2Seq. Following previous work, we\nevaluate our model on a popular Chinese social media dataset. Experimental\nresults show that our model achieves the state-of-the-art performances on the\nbenchmark dataset.","url_abs":"http://arxiv.org/abs/1805.04869v1","url_pdf":"http://arxiv.org/pdf/1805.04869v1.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":"autoencoder-as-assistant-supervisor-improving","repo_url":"https://github.com/lancopku/superAE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"abstractive-text-summarization","task_name":"Abstractive Text Summarization"},{"task_slug":"text-summarization","task_name":"Text Summarization"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"seq2seq","method_name":"Seq2Seq"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}