{"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/entity-commonsense-representation-for-neural","title":"Entity Commonsense Representation for Neural Abstractive Summarization","arxiv_id":"1806.05504","date":"2018-06-14","proceeding":"NAACL 2018 6","authors":["Reinald Kim Amplayo","Seonjae Lim","Seung-won Hwang"],"abstract":"A major proportion of a text summary includes important entities found in the\noriginal text. These entities build up the topic of the summary. Moreover, they\nhold commonsense information once they are linked to a knowledge base. Based on\nthese observations, this paper investigates the usage of linked entities to\nguide the decoder of a neural text summarizer to generate concise and better\nsummaries. To this end, we leverage on an off-the-shelf entity linking system\n(ELS) to extract linked entities and propose Entity2Topic (E2T), a module\neasily attachable to a sequence-to-sequence model that transforms a list of\nentities into a vector representation of the topic of the summary. Current\navailable ELS's are still not sufficiently effective, possibly introducing\nunresolved ambiguities and irrelevant entities. We resolve the imperfections of\nthe ELS by (a) encoding entities with selective disambiguation, and (b) pooling\nentity vectors using firm attention. By applying E2T to a simple\nsequence-to-sequence model with attention mechanism as base model, we see\nsignificant improvements of the performance in the Gigaword (sentence to title)\nand CNN (long document to multi-sentence highlights) summarization datasets by\nat least 2 ROUGE points.","url_abs":"http://arxiv.org/abs/1806.05504v1","url_pdf":"http://arxiv.org/pdf/1806.05504v1.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":"entity-commonsense-representation-for-neural","repo_url":"https://github.com/rktamplayo/Entity2Topic","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"abstractive-text-summarization","task_name":"Abstractive Text Summarization"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"entity-linking","task_name":"Entity Linking"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/text-summarization-on-gigaword","task":"Text Summarization","dataset":"GigaWord","model":"Seq2seq + E2T_cnn","rank_in_archive_order":26,"of":41,"metrics":{"ROUGE-1":"37.04","ROUGE-2":"16.66","ROUGE-L":"34.93"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}