{"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/paged-a-benchmark-for-procedural-graphs","title":"PAGED: A Benchmark for Procedural Graphs Extraction from Documents","arxiv_id":"2408.03630","date":"2024-08-07","proceeding":null,"authors":["Weihong Du","Wenrui Liao","Hongru Liang","Wenqiang Lei"],"abstract":"Automatic extraction of procedural graphs from documents creates a low-cost way for users to easily understand a complex procedure by skimming visual graphs. Despite the progress in recent studies, it remains unanswered: whether the existing studies have well solved this task (Q1) and whether the emerging large language models (LLMs) can bring new opportunities to this task (Q2). To this end, we propose a new benchmark PAGED, equipped with a large high-quality dataset and standard evaluations. It investigates five state-of-the-art baselines, revealing that they fail to extract optimal procedural graphs well because of their heavy reliance on hand-written rules and limited available data. We further involve three advanced LLMs in PAGED and enhance them with a novel self-refine strategy. The results point out the advantages of LLMs in identifying textual elements and their gaps in building logical structures. We hope PAGED can serve as a major landmark for automatic procedural graph extraction and the investigations in PAGED can offer insights into the research on logic reasoning among non-sequential elements.","url_abs":"https://arxiv.org/abs/2408.03630v2","url_pdf":"https://arxiv.org/pdf/2408.03630v2.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":"paged-a-benchmark-for-procedural-graphs","repo_url":"https://github.com/scunlp/paged","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2408.03630","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.03630"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/SCUNLP/PAGED","reach":{"status":"ok","spdx":"Apache-2.0"}}],"summary":{"ran":3,"unverified":3},"by_repo_kind":{"official":{"samples":6,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"2f0c752245ef36cf","entry":"get_gateway_content","repo":"SCUNLP/PAGED","repo_kind":"official","path":"evaluation/metric/lexical_based.py","file_url":"https://github.com/SCUNLP/PAGED/blob/HEAD/evaluation/metric/lexical_based.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"2f0c752245ef36cf"}},{"code_sha256_prefix":"1508609ea2a11370","entry":"lexical_score","repo":"SCUNLP/PAGED","repo_kind":"official","path":"evaluation/metric/lexical_based.py","file_url":"https://github.com/SCUNLP/PAGED/blob/HEAD/evaluation/metric/lexical_based.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"1508609ea2a11370"}},{"code_sha256_prefix":"19e2c52a2f095881","entry":"parse_model_output","repo":"SCUNLP/PAGED","repo_kind":"official","path":"evaluation/metric/lexical_based.py","file_url":"https://github.com/SCUNLP/PAGED/blob/HEAD/evaluation/metric/lexical_based.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"19e2c52a2f095881"}},{"code_sha256_prefix":"96dbccf63610e4c5","entry":"data_prepare_hf_format","repo":"SCUNLP/PAGED","repo_kind":"official","path":"evaluation/baselines/prepare_traning_data.py","file_url":"https://github.com/SCUNLP/PAGED/blob/HEAD/evaluation/baselines/prepare_traning_data.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"96dbccf63610e4c5"}},{"code_sha256_prefix":"300361053ffe8296","entry":"eva_trans2triples","repo":"SCUNLP/PAGED","repo_kind":"official","path":"evaluation/eva_utils/eva_graph2triple.py","file_url":"https://github.com/SCUNLP/PAGED/blob/HEAD/evaluation/eva_utils/eva_graph2triple.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"300361053ffe8296"}},{"code_sha256_prefix":"ae811ccdb077a784","entry":"trans2triples","repo":"SCUNLP/PAGED","repo_kind":"official","path":"evaluation/baselines/prepare_traning_data.py","file_url":"https://github.com/SCUNLP/PAGED/blob/HEAD/evaluation/baselines/prepare_traning_data.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"ae811ccdb077a784"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}