{"id":"https://openalex.org/W2888929163","doi":"https://doi.org/10.1145/3299869.3300074","title":"Verifying Text Summaries of Relational Data Sets","display_name":"Verifying Text Summaries of Relational Data Sets","publication_year":2019,"publication_date":"2019-06-18","ids":{"openalex":"https://openalex.org/W2888929163","doi":"https://doi.org/10.1145/3299869.3300074","mag":"2888929163"},"language":"en","primary_location":{"id":"doi:10.1145/3299869.3300074","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3299869.3300074","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3299869.3300074","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 International Conference on Management of Data","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3299869.3300074","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102329375","display_name":"Saehan Jo","orcid":null},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Saehan Jo","raw_affiliation_strings":["Cornell University, Ithaca, NY, USA"],"affiliations":[{"raw_affiliation_string":"Cornell University, Ithaca, NY, USA","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087259526","display_name":"Immanuel Trummer","orcid":"https://orcid.org/0000-0002-7203-2349"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Immanuel Trummer","raw_affiliation_strings":["Cornell University, Ithaca, NY, USA"],"affiliations":[{"raw_affiliation_string":"Cornell University, Ithaca, NY, USA","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008869138","display_name":"Weicheng Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Weicheng Yu","raw_affiliation_strings":["Cornell University, Ithaca, NY, USA"],"affiliations":[{"raw_affiliation_string":"Cornell University, Ithaca, NY, USA","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040824554","display_name":"Xuezhi Wang","orcid":"https://orcid.org/0000-0001-5222-248X"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xuezhi Wang","raw_affiliation_strings":["Google Research, New York, NY, USA"],"affiliations":[{"raw_affiliation_string":"Google Research, New York, NY, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043275971","display_name":"Cong Yu","orcid":"https://orcid.org/0000-0001-7331-2345"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Cong Yu","raw_affiliation_strings":["Google Research, New York, NY, USA"],"affiliations":[{"raw_affiliation_string":"Google Research, New York, NY, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101579256","display_name":"Daniel Liu","orcid":"https://orcid.org/0000-0002-2385-2957"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Daniel Liu","raw_affiliation_strings":["Cornell University, Ithaca, NY, USA"],"affiliations":[{"raw_affiliation_string":"Cornell University, Ithaca, NY, USA","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003829139","display_name":"Niyati Mehta","orcid":null},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Niyati Mehta","raw_affiliation_strings":["Cornell University, Ithaca, NY, USA"],"affiliations":[{"raw_affiliation_string":"Cornell University, Ithaca, NY, USA","institution_ids":["https://openalex.org/I205783295"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5102329375"],"corresponding_institution_ids":["https://openalex.org/I205783295"],"apc_list":null,"apc_paid":null,"fwci":1.6897952,"has_fulltext":true,"cited_by_count":16,"citation_normalized_percentile":{"value":0.86940614,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"299","last_page":"316"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10215","display_name":"Semantic Web and Ontologies","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10028","display_name":"Topic Modeling","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/computer-science","display_name":"Computer science","score":0.8775239586830139},{"id":"https://openalex.org/keywords/correctness","display_name":"Correctness","score":0.6733380556106567},{"id":"https://openalex.org/keywords/sql","display_name":"SQL","score":0.5940462946891785},{"id":"https://openalex.org/keywords/query-language","display_name":"Query language","score":0.5606541633605957},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.5092705488204956},{"id":"https://openalex.org/keywords/relational-database","display_name":"Relational database","score":0.5078697800636292},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4885350167751312},{"id":"https://openalex.org/keywords/query-by-example","display_name":"Query by Example","score":0.48613688349723816},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.46646296977996826},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.44135037064552307},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.42150694131851196},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3350854218006134},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.3322793245315552},{"id":"https://openalex.org/keywords/web-search-query","display_name":"Web search query","score":0.2700660824775696},{"id":"https://openalex.org/keywords/search-engine","display_name":"Search engine","score":0.10959851741790771}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8775239586830139},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.6733380556106567},{"id":"https://openalex.org/C510870499","wikidata":"https://www.wikidata.org/wiki/Q47607","display_name":"SQL","level":2,"score":0.5940462946891785},{"id":"https://openalex.org/C192028432","wikidata":"https://www.wikidata.org/wiki/Q845739","display_name":"Query language","level":2,"score":0.5606541633605957},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.5092705488204956},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.5078697800636292},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4885350167751312},{"id":"https://openalex.org/C194222762","wikidata":"https://www.wikidata.org/wiki/Q114486","display_name":"Query by Example","level":4,"score":0.48613688349723816},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.46646296977996826},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.44135037064552307},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.42150694131851196},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3350854218006134},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.3322793245315552},{"id":"https://openalex.org/C164120249","wikidata":"https://www.wikidata.org/wiki/Q995982","display_name":"Web search query","level":3,"score":0.2700660824775696},{"id":"https://openalex.org/C97854310","wikidata":"https://www.wikidata.org/wiki/Q19541","display_name":"Search engine","level":2,"score":0.10959851741790771}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3299869.3300074","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3299869.3300074","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3299869.3300074","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 International Conference on Management of Data","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3299869.3300074","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3299869.3300074","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3299869.3300074","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 International Conference on Management of Data","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.8199999928474426}],"awards":[{"id":"https://openalex.org/G7660426423","display_name":null,"funder_award_id":"Huawei-Cornell Research Initiative","funder_id":"https://openalex.org/F4320322183","funder_display_name":"Huawei Technologies"}],"funders":[{"id":"https://openalex.org/F4320322183","display_name":"Huawei Technologies","ror":"https://ror.org/00cmhce21"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2888929163.pdf","grobid_xml":"https://content.openalex.org/works/W2888929163.grobid-xml"},"referenced_works_count":39,"referenced_works":["https://openalex.org/W96289099","https://openalex.org/W651477617","https://openalex.org/W1531374185","https://openalex.org/W1532503642","https://openalex.org/W1543836204","https://openalex.org/W1971684273","https://openalex.org/W2011427077","https://openalex.org/W2032374895","https://openalex.org/W2038721957","https://openalex.org/W2066288042","https://openalex.org/W2081580037","https://openalex.org/W2121350579","https://openalex.org/W2123442489","https://openalex.org/W2154986869","https://openalex.org/W2166348853","https://openalex.org/W2227869055","https://openalex.org/W2236647290","https://openalex.org/W2250293237","https://openalex.org/W2250843058","https://openalex.org/W2251214593","https://openalex.org/W2258921667","https://openalex.org/W2266769560","https://openalex.org/W2269738476","https://openalex.org/W2274833265","https://openalex.org/W2327805699","https://openalex.org/W2340645138","https://openalex.org/W2508114268","https://openalex.org/W2508865106","https://openalex.org/W2548149136","https://openalex.org/W2567809919","https://openalex.org/W2607618454","https://openalex.org/W2621778545","https://openalex.org/W2743800013","https://openalex.org/W2751368487","https://openalex.org/W2760347205","https://openalex.org/W2788784374","https://openalex.org/W2963416784","https://openalex.org/W2963857245","https://openalex.org/W3105663928"],"related_works":["https://openalex.org/W2573939812","https://openalex.org/W151494989","https://openalex.org/W1548279772","https://openalex.org/W2897196530","https://openalex.org/W319014924","https://openalex.org/W2120098008","https://openalex.org/W1488984669","https://openalex.org/W1639806124","https://openalex.org/W2031915568","https://openalex.org/W4285818047"],"abstract_inverted_index":{"We":[0,161],"present":[1],"a":[2,32,40,65,75,92,111,128,180],"novel":[3],"natural":[4,22],"language":[5,23],"query":[6,30,34,97,136],"interface,":[7],"the":[8,43,55,59,62,71,83,115],"AggChecker,":[9],"aimed":[10],"at":[11],"text":[12,47,89,118],"summaries":[13],"of":[14,61,105,107,182],"relational":[15],"data":[16],"sets.":[17],"The":[18],"tool":[19,174],"focuses":[20],"on":[21,79,165],"claims":[24,119,177],"that":[25,49,68],"translate":[26],"into":[27],"an":[28,143],"SQL":[29],"and":[31,82,138,192],"claimed":[33],"result.":[35],"Similar":[36],"in":[37,74,146,178],"spirit":[38],"to":[39,51,91,103,120,155],"spell":[41],"checker,":[42],"AggChecker":[44,186],"marks":[45],"up":[46],"passages":[48],"seem":[50],"be":[52],"inconsistent":[53],"with":[54],"actual":[56],"data.":[57],"At":[58],"heart":[60],"system":[63,116,164],"is":[64,142],"probabilistic":[66],"model":[67],"reasons":[69],"about":[70],"input":[72,113],"document":[73,84],"holistic":[76],"fashion.":[77],"Based":[78],"claim":[80,90],"keywords":[81],"structure,":[85],"it":[86],"maps":[87,117],"each":[88],"probability":[93],"distribution":[94],"over":[95],"associated":[96],"translations.":[98],"By":[99],"efficiently":[100],"executing":[101],"tens":[102],"hundreds":[104],"thousands":[106],"candidate":[108],"translations":[109],"for":[110],"typical":[112],"document,":[114],"correctness":[121],"probabilities.":[122],"This":[123],"process":[124,145],"becomes":[125],"practical":[126],"via":[127,135],"specialized":[129],"processing":[130],"backend,":[131],"avoiding":[132],"redundant":[133],"work":[134],"merging":[137],"result":[139],"caching.":[140],"Verification":[141],"interactive":[144],"which":[147],"users":[148],"are":[149],"shown":[150],"tentative":[151],"results,":[152],"enabling":[153],"them":[154],"take":[156],"corrective":[157],"actions":[158],"if":[159],"necessary.":[160],"tested":[162],"our":[163],"53":[166],"publicly":[167],"available":[168],"articles":[169],"containing":[170],"392":[171],"claims.":[172],"Our":[173],"revealed":[175],"erroneous":[176],"roughly":[179],"third":[181],"test":[183],"cases.":[184],"Also,":[185],"compares":[187],"favorably":[188],"against":[189],"several":[190],"automated":[191],"semi-automated":[193],"fact":[194],"checking":[195],"baselines.":[196]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}