{"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/tap-dlnd-10-a-corpus-for-document-level","title":"TAP-DLND 1.0 : A Corpus for Document Level Novelty Detection","arxiv_id":"1802.06950","date":"2018-02-20","proceeding":"LREC 2018 5","authors":["Tirthankar Ghosal","Amitra Salam","Swati Tiwari","Asif Ekbal","Pushpak Bhattacharyya"],"abstract":"Detecting novelty of an entire document is an Artificial Intelligence (AI)\nfrontier problem that has widespread NLP applications, such as extractive\ndocument summarization, tracking development of news events, predicting impact\nof scholarly articles, etc. Important though the problem is, we are unaware of\nany benchmark document level data that correctly addresses the evaluation of\nautomatic novelty detection techniques in a classification framework. To bridge\nthis gap, we present here a resource for benchmarking the techniques for\ndocument level novelty detection. We create the resource via event-specific\ncrawling of news documents across several domains in a periodic manner. We\nrelease the annotated corpus with necessary statistics and show its use with a\ndeveloped system for the problem in concern.","url_abs":"http://arxiv.org/abs/1802.06950v1","url_pdf":"http://arxiv.org/pdf/1802.06950v1.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":"tap-dlnd-10-a-corpus-for-document-level","repo_url":"https://github.com/Pavyel/Novelty-Detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"tap-dlnd-10-a-corpus-for-document-level","repo_url":"https://github.com/edithal-14/A-Deep-Neural-Solution-To-Document-Level-Novelty-Detection-COLING-2018-","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"articles","task_name":"Articles"},{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"document-summarization","task_name":"Document Summarization"},{"task_slug":"extractive-document-summarization-1","task_name":"Extractive Document Summarization"},{"task_slug":"extractive-document-summarization","task_name":"Extractive Text Summarization"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"novelty-detection","task_name":"Novelty Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.06950","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}