{"id":"https://openalex.org/W3061754956","doi":"https://doi.org/10.1145/3340531.3411943","title":"Neural Formatting for Spreadsheet Tables","display_name":"Neural Formatting for Spreadsheet Tables","publication_year":2020,"publication_date":"2020-10-19","ids":{"openalex":"https://openalex.org/W3061754956","doi":"https://doi.org/10.1145/3340531.3411943","mag":"3061754956"},"language":"en","primary_location":{"id":"doi:10.1145/3340531.3411943","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3340531.3411943","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM International Conference on Information &amp; Knowledge Management","raw_type":"proceedings-article"},"type":"conference-paper","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/A5102019411","display_name":"Haoyu Dong","orcid":"https://orcid.org/0009-0007-5003-6801"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haoyu Dong","raw_affiliation_strings":["Microsoft Research, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Research, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101504448","display_name":"Jinyu Wang","orcid":"https://orcid.org/0009-0003-4843-8597"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinyu Wang","raw_affiliation_strings":["Microsoft Research, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Research, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112557825","display_name":"Zhouyu Fu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhouyu Fu","raw_affiliation_strings":["Microsoft Research, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Research, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006300825","display_name":"Shi Han","orcid":"https://orcid.org/0000-0002-0360-6089"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shi Han","raw_affiliation_strings":["Microsoft Research, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Research, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100331488","display_name":"Dongmei Zhang","orcid":"https://orcid.org/0000-0002-9230-2799"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongmei Zhang","raw_affiliation_strings":["Microsoft Research, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Research, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210113369"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"305","last_page":"314"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9919999837875366,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9919999837875366,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9915000200271606,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10799","display_name":"Data Visualization and Analytics","score":0.9898999929428101,"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/disk-formatting","display_name":"Disk formatting","score":0.9769461154937744},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8278615474700928},{"id":"https://openalex.org/keywords/table","display_name":"Table (database)","score":0.7006780505180359},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5289114713668823},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4711555242538452},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45722097158432007},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4510487914085388},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4400363862514496},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.40167611837387085},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3583596348762512}],"concepts":[{"id":"https://openalex.org/C88006597","wikidata":"https://www.wikidata.org/wiki/Q690117","display_name":"Disk formatting","level":2,"score":0.9769461154937744},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8278615474700928},{"id":"https://openalex.org/C45235069","wikidata":"https://www.wikidata.org/wiki/Q278425","display_name":"Table (database)","level":2,"score":0.7006780505180359},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5289114713668823},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4711555242538452},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45722097158432007},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4510487914085388},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4400363862514496},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.40167611837387085},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3583596348762512},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3340531.3411943","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3340531.3411943","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM International Conference on Information &amp; Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W141724566","https://openalex.org/W1511986666","https://openalex.org/W1533946607","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2032655922","https://openalex.org/W2099471712","https://openalex.org/W2125389028","https://openalex.org/W2139893662","https://openalex.org/W2163605009","https://openalex.org/W2271551547","https://openalex.org/W2475287302","https://openalex.org/W2519536754","https://openalex.org/W2559655401","https://openalex.org/W2593768305","https://openalex.org/W2604225376","https://openalex.org/W2604272474","https://openalex.org/W2886362482","https://openalex.org/W2902900188","https://openalex.org/W2914121031","https://openalex.org/W2951939904","https://openalex.org/W2963420272","https://openalex.org/W2963522749","https://openalex.org/W2963767194","https://openalex.org/W2963800363","https://openalex.org/W2968970819","https://openalex.org/W3031454183","https://openalex.org/W3173700007","https://openalex.org/W6600739665"],"related_works":["https://openalex.org/W4244466418","https://openalex.org/W2104062382","https://openalex.org/W2162878363","https://openalex.org/W2389021890","https://openalex.org/W2479325685","https://openalex.org/W4245101192","https://openalex.org/W2146588098","https://openalex.org/W3183791698","https://openalex.org/W3111710556","https://openalex.org/W2480873127"],"abstract_inverted_index":{"Spreadsheets":[0],"are":[1],"popular":[2],"and":[3,9,20,36,51,66,106,130],"widely":[4],"used":[5,152],"for":[6,31,64,165],"data":[7,37],"presentation":[8],"management,":[10],"where":[11],"users":[12],"create":[13],"tables":[14,89,167],"in":[15,90,153],"various":[16],"structures":[17,35],"to":[18,110],"organize":[19],"present":[21],"data.":[22],"Table":[23],"formatting":[24,47,62,85],"is":[25,159],"an":[26],"important":[27],"yet":[28],"tedious":[29,50],"task":[30],"better":[32],"exhibiting":[33],"table":[34,84],"relationships.":[38],"However,":[39],"without":[40,94],"the":[41,114,119,149,160],"aid":[42],"of":[43,69,121,148],"intelligent":[44],"tools,":[45],"manual":[46],"remains":[48],"a":[49,60,74,91],"time-consuming":[52],"task.":[53],"In":[54,98],"this":[55],"paper,":[56],"we":[57,100],"propose":[58],"CellGAN,":[59],"neural":[61],"model":[63],"learning":[65],"recommending":[67],"formats":[68],"spreadsheet":[70,88,115,166],"tables.":[71],"Based":[72],"on":[73],"novel":[75],"conditional":[76],"generative":[77],"adversarial":[78],"network":[79],"(cGAN)":[80],"architecture,":[81],"CellGAN":[82,99,122],"learns":[83],"from":[86,113],"real-world":[87,124],"self-supervised":[92],"fashion":[93],"requiring":[95],"human":[96,131],"labeling.":[97],"devise":[101],"two":[102],"mechanisms,":[103],"row/column-wise":[104],"pooling":[105],"local":[107],"refinement":[108],"network,":[109],"address":[111],"challenges":[112],"domain.":[116],"We":[117],"evaluate":[118],"effectiveness":[120],"against":[123],"datasets":[125],"using":[126],"both":[127],"quantitative":[128],"metrics":[129],"perception":[132],"studies.":[133],"The":[134],"results":[135],"indicate":[136],"remarkable":[137],"performance":[138],"gains":[139],"over":[140],"rule-based":[141],"methods,":[142],"graphical":[143],"models":[144],"or":[145],"direct":[146],"application":[147],"state-of-the-art":[150],"cGANs":[151],"visual":[154],"synthesis":[155],"tasks.":[156],"Neural":[157],"Formatting":[158],"first":[161],"step":[162],"towards":[163],"auto-formatting":[164],"with":[168],"promising":[169],"results.":[170]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
