{"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/kpi-edgar-a-novel-dataset-and-accompanying","title":"KPI-EDGAR: A Novel Dataset and Accompanying Metric for Relation Extraction from Financial Documents","arxiv_id":"2210.09163","date":"2022-10-17","proceeding":null,"authors":["Tobias Deußer","Syed Musharraf Ali","Lars Hillebrand","Desiana Nurchalifah","Basil Jacob","Christian Bauckhage","Rafet Sifa"],"abstract":"We introduce KPI-EDGAR, a novel dataset for Joint Named Entity Recognition and Relation Extraction building on financial reports uploaded to the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system, where the main objective is to extract Key Performance Indicators (KPIs) from financial documents and link them to their numerical values and other attributes. We further provide four accompanying baselines for benchmarking potential future research. Additionally, we propose a new way of measuring the success of said extraction process by incorporating a word-level weighting scheme into the conventional F1 score to better model the inherently fuzzy borders of the entity pairs of a relation in this domain.","url_abs":"https://arxiv.org/abs/2210.09163v1","url_pdf":"https://arxiv.org/pdf/2210.09163v1.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":"kpi-edgar-a-novel-dataset-and-accompanying","repo_url":"https://github.com/tobideusser/kpi-edgar","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"joint-entity-and-relation-extraction","task_name":"Joint Entity and Relation Extraction"},{"task_slug":"named-entity-recognition-1","task_name":"Named Entity Recognition"},{"task_slug":"named-entity-recognition-ner","task_name":"Named Entity Recognition (NER)"},{"task_slug":null,"task_name":"Relation"},{"task_slug":"relation-extraction","task_name":"Relation Extraction"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"named-entity-recognition","task_name":"named-entity-recognition"}],"methods":[],"datasets_introduced":[{"slug":"kpi-edgar","name":"KPI-EDGAR","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/joint-entity-and-relation-extraction-on-kpi","task":"Joint Entity and Relation Extraction","dataset":"KPI-EDGAR","model":"KPI-BERT","rank_in_archive_order":1,"of":1,"metrics":{"Relation F1":"43.76"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}