{"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/unsupervised-sentence-compression-using","title":"Unsupervised Sentence Compression using Denoising Auto-Encoders","arxiv_id":"1809.02669","date":"2018-09-07","proceeding":"CONLL 2018 10","authors":["Thibault Févry","Jason Phang"],"abstract":"In sentence compression, the task of shortening sentences while retaining the\noriginal meaning, models tend to be trained on large corpora containing pairs\nof verbose and compressed sentences. To remove the need for paired corpora, we\nemulate a summarization task and add noise to extend sentences and train a\ndenoising auto-encoder to recover the original, constructing an end-to-end\ntraining regime without the need for any examples of compressed sentences. We\nconduct a human evaluation of our model on a standard text summarization\ndataset and show that it performs comparably to a supervised baseline based on\ngrammatical correctness and retention of meaning. Despite being exposed to no\ntarget data, our unsupervised models learn to generate imperfect but reasonably\nreadable sentence summaries. Although we underperform supervised models based\non ROUGE scores, our models are competitive with a supervised baseline based on\nhuman evaluation for grammatical correctness and retention of meaning.","url_abs":"http://arxiv.org/abs/1809.02669v1","url_pdf":"http://arxiv.org/pdf/1809.02669v1.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":"unsupervised-sentence-compression-using","repo_url":"https://github.com/zphang/usc_dae","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentence-compression","task_name":"Sentence Compression"},{"task_slug":"text-summarization","task_name":"Text Summarization"},{"task_slug":"unsupervised-sentence-compression","task_name":"Unsupervised Sentence Compression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.02669","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}