{"id":"https://openalex.org/W4416251265","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228526","title":"TabMeta: A Novel Framework for Chart Generation Using Metadata and Enhanced Column Attention","display_name":"TabMeta: A Novel Framework for Chart Generation Using Metadata and Enhanced Column Attention","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416251265","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228526"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228526","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228526","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5018588461","display_name":"Yuxing Tan","orcid":null},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuxing Tan","raw_affiliation_strings":["Xinjiang University,School of Software,Urumqi,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xinjiang University,School of Software,Urumqi,China","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100745388","display_name":"Liang He","orcid":"https://orcid.org/0000-0002-6394-8531"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang He","raw_affiliation_strings":["Tsinghua University,Department of Electronic Engineeringm,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Department of Electronic Engineeringm,Beijing,China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023912573","display_name":"Xiaoding Qi","orcid":"https://orcid.org/0000-0002-7105-1193"},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaozhe Qi","raw_affiliation_strings":["Xinjiang University,School of Software,Urumqi,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xinjiang University,School of Software,Urumqi,China","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhida Song","orcid":null},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhida Song","raw_affiliation_strings":["Xinjiang University,School of Computer Science and Technology,Urumqi,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xinjiang University,School of Computer Science and Technology,Urumqi,China","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"last","author":{"id":null,"display_name":"Tengfei Feng","orcid":null},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tengfei Feng","raw_affiliation_strings":["Xinjiang University,School of Computer Science and Technology,Urumqi,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xinjiang University,School of Computer Science and Technology,Urumqi,China","institution_ids":["https://openalex.org/I96908189"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.30323135,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.5184000134468079,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.5184000134468079,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11719","display_name":"Data Quality and Management","score":0.27639999985694885,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.04749999940395355,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/metadata","display_name":"Metadata","score":0.8799999952316284},{"id":"https://openalex.org/keywords/column","display_name":"Column (typography)","score":0.7141000032424927},{"id":"https://openalex.org/keywords/table","display_name":"Table (database)","score":0.7059000134468079},{"id":"https://openalex.org/keywords/chart","display_name":"Chart","score":0.6201000213623047},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.4717999994754791},{"id":"https://openalex.org/keywords/data-extraction","display_name":"Data extraction","score":0.3955000042915344},{"id":"https://openalex.org/keywords/row","display_name":"Row","score":0.3427000045776367}],"concepts":[{"id":"https://openalex.org/C93518851","wikidata":"https://www.wikidata.org/wiki/Q180160","display_name":"Metadata","level":2,"score":0.8799999952316284},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8220999836921692},{"id":"https://openalex.org/C2780551164","wikidata":"https://www.wikidata.org/wiki/Q2306599","display_name":"Column (typography)","level":3,"score":0.7141000032424927},{"id":"https://openalex.org/C45235069","wikidata":"https://www.wikidata.org/wiki/Q278425","display_name":"Table (database)","level":2,"score":0.7059000134468079},{"id":"https://openalex.org/C190812933","wikidata":"https://www.wikidata.org/wiki/Q28923","display_name":"Chart","level":2,"score":0.6201000213623047},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5458999872207642},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.5260000228881836},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4717999994754791},{"id":"https://openalex.org/C2777466982","wikidata":"https://www.wikidata.org/wiki/Q5227287","display_name":"Data extraction","level":3,"score":0.3955000042915344},{"id":"https://openalex.org/C135598885","wikidata":"https://www.wikidata.org/wiki/Q1366302","display_name":"Row","level":2,"score":0.3427000045776367},{"id":"https://openalex.org/C138958017","wikidata":"https://www.wikidata.org/wiki/Q190087","display_name":"Data type","level":2,"score":0.33719998598098755},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3361000120639801},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.328000009059906},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.2971000075340271},{"id":"https://openalex.org/C30872290","wikidata":"https://www.wikidata.org/wiki/Q1172389","display_name":"Data element","level":3,"score":0.2808000147342682},{"id":"https://openalex.org/C153048206","wikidata":"https://www.wikidata.org/wiki/Q3454922","display_name":"Metadata repository","level":3,"score":0.26570001244544983},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.2606000006198883},{"id":"https://openalex.org/C2776187449","wikidata":"https://www.wikidata.org/wiki/Q1513879","display_name":"Natural language generation","level":3,"score":0.2535000145435333}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228526","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228526","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320329860","display_name":"National Science and Technology Major Project","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1552847225","https://openalex.org/W1740034975","https://openalex.org/W2008120802","https://openalex.org/W2080133951","https://openalex.org/W2516678343","https://openalex.org/W2795226127","https://openalex.org/W2883394174","https://openalex.org/W2946535156","https://openalex.org/W2951621897","https://openalex.org/W2963794306","https://openalex.org/W3035140194","https://openalex.org/W3081758821","https://openalex.org/W3129639992","https://openalex.org/W3158303960","https://openalex.org/W3169230937","https://openalex.org/W4205922070","https://openalex.org/W4221041757","https://openalex.org/W4389524441"],"related_works":[],"abstract_inverted_index":{"Data":[0],"analysis":[1],"using":[2],"tables":[3,39],"and":[4,34,79,82,107,131,140],"graphs":[5],"is":[6],"fundamental":[7],"to":[8,24,30,89],"research":[9],"across":[10],"disciplines.":[11],"While":[12],"pre-trained":[13],"language":[14],"models":[15],"have":[16],"shown":[17],"potential":[18],"in":[19,36,98,126,129,133,143],"table":[20,75,145],"understanding,":[21],"their":[22],"application":[23],"chart":[25,62,117],"generation":[26],"remains":[27],"underexplored":[28],"due":[29],"limited":[31],"table-to-chart":[32],"datasets":[33],"challenges":[35],"encoding":[37],"two-dimensional":[38],"into":[40],"one-dimensional":[41],"representations.":[42],"To":[43],"address":[44],"these,":[45],"we":[46],"introduce":[47],"SmallT2F,":[48],"a":[49,68,95,121],"curated":[50],"dataset":[51],"of":[52,124,138],"tabular":[53],"data":[54,108],"from":[55],"leading":[56],"academic":[57],"journals":[58],"annotated":[59],"with":[60,120],"corresponding":[61],"types.":[63],"We":[64],"also":[65],"propose":[66],"TabMeta,":[67],"hybrid":[69],"metadata":[70,81,139],"extraction":[71],"framework":[72],"that":[73,113],"encodes":[74],"structures":[76],"through":[77],"row":[78],"column":[80],"incorporates":[83],"an":[84],"enhanced":[85],"column-aware":[86],"attention":[87],"mechanism":[88],"capture":[90],"spatial":[91],"relationships.":[92],"By":[93],"introducing":[94],"bias":[96],"term":[97],"the":[99,136],"attention-scoring":[100],"function,":[101],"TabMeta":[102,114],"strengthens":[103],"interactions":[104],"between":[105],"metric":[106],"columns.":[109],"Experimental":[110],"results":[111],"demonstrate":[112],"significantly":[115],"improves":[116],"type":[118],"selection,":[119],"minimum":[122],"improvement":[123],"20%":[125],"Precision,":[127],"18.6%":[128],"Recall,":[130],"19.4%":[132],"F1-Score,":[134],"underscoring":[135],"importance":[137],"inter-column":[141],"relationships":[142],"leveraging":[144],"structure.":[146]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-11-14T00:00:00"}