{"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/abstractive-summarization-of-reddit-posts","title":"Abstractive Summarization of Reddit Posts with Multi-level Memory Networks","arxiv_id":"1811.00783","date":"2018-11-02","proceeding":"NAACL 2019 6","authors":["Byeongchang Kim","Hyunwoo Kim","Gunhee Kim"],"abstract":"We address the problem of abstractive summarization in two directions:\nproposing a novel dataset and a new model. First, we collect Reddit TIFU\ndataset, consisting of 120K posts from the online discussion forum Reddit. We\nuse such informal crowd-generated posts as text source, in contrast with\nexisting datasets that mostly use formal documents as source such as news\narticles. Thus, our dataset could less suffer from some biases that key\nsentences usually locate at the beginning of the text and favorable summary\ncandidates are already inside the text in similar forms. Second, we propose a\nnovel abstractive summarization model named multi-level memory networks (MMN),\nequipped with multi-level memory to store the information of text from\ndifferent levels of abstraction. With quantitative evaluation and user studies\nvia Amazon Mechanical Turk, we show the Reddit TIFU dataset is highly\nabstractive and the MMN outperforms the state-of-the-art summarization models.","url_abs":"http://arxiv.org/abs/1811.00783v2","url_pdf":"http://arxiv.org/pdf/1811.00783v2.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":"abstractive-summarization-of-reddit-posts","repo_url":"https://github.com/ctr4si/MMN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"abstractive-text-summarization","task_name":"Abstractive Text Summarization"},{"task_slug":"articles","task_name":"Articles"}],"methods":[],"datasets_introduced":[{"slug":"reddit-tifu","name":"Reddit TIFU","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.00783","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}