{"id":"https://openalex.org/W2949167374","doi":"https://doi.org/10.18653/v1/p19-1657","title":"Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-ray Reports","display_name":"Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-ray Reports","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2949167374","doi":"https://doi.org/10.18653/v1/p19-1657","mag":"2949167374"},"language":"en","primary_location":{"id":"doi:10.18653/v1/p19-1657","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p19-1657","pdf_url":"https://www.aclweb.org/anthology/P19-1657.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/P19-1657.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Baoyu Jing","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Baoyu Jing","raw_affiliation_strings":["Petuum Inc., USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Petuum Inc., USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zeya Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zeya Wang","raw_affiliation_strings":["Petuum Inc., USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Petuum Inc., USA","institution_ids":[]}]},{"author_position":"last","author":{"id":null,"display_name":"Eric Xing","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Eric Xing","raw_affiliation_strings":["Petuum Inc., USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Petuum Inc., USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.0881,"has_fulltext":true,"cited_by_count":124,"citation_normalized_percentile":{"value":0.95082727,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"6570","last_page":"6580"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9994000196456909,"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/T10028","display_name":"Topic Modeling","score":0.9994000196456909,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9991999864578247,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9879999756813049,"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/automatic-summarization","display_name":"Automatic summarization","score":0.8126000165939331},{"id":"https://openalex.org/keywords/normality","display_name":"Normality","score":0.6154999732971191},{"id":"https://openalex.org/keywords/abnormality","display_name":"Abnormality","score":0.5507000088691711},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5300999879837036},{"id":"https://openalex.org/keywords/section","display_name":"Section (typography)","score":0.5094000101089478},{"id":"https://openalex.org/keywords/workload","display_name":"Workload","score":0.4814000129699707}],"concepts":[{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.8126000165939331},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7675999999046326},{"id":"https://openalex.org/C2776157432","wikidata":"https://www.wikidata.org/wiki/Q1375683","display_name":"Normality","level":2,"score":0.6154999732971191},{"id":"https://openalex.org/C50965678","wikidata":"https://www.wikidata.org/wiki/Q2724302","display_name":"Abnormality","level":2,"score":0.5507000088691711},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5300999879837036},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.516700029373169},{"id":"https://openalex.org/C2780129039","wikidata":"https://www.wikidata.org/wiki/Q1931107","display_name":"Section (typography)","level":2,"score":0.5094000101089478},{"id":"https://openalex.org/C2778476105","wikidata":"https://www.wikidata.org/wiki/Q628539","display_name":"Workload","level":2,"score":0.4814000129699707},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4246000051498413},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.413100004196167},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.3944999873638153},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.3465999960899353},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.33970001339912415},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29010000824928284},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.28780001401901245},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.26840001344680786},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.26339998841285706}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.18653/v1/p19-1657","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p19-1657","pdf_url":"https://www.aclweb.org/anthology/P19-1657.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2004.12274","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2004.12274","pdf_url":"https://arxiv.org/pdf/2004.12274","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.18653/v1/p19-1657","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p19-1657","pdf_url":"https://www.aclweb.org/anthology/P19-1657.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2384888906","https://openalex.org/W2144190808","https://openalex.org/W2101955803","https://openalex.org/W2376314740","https://openalex.org/W2366644548","https://openalex.org/W2469626427","https://openalex.org/W2357241418","https://openalex.org/W2119214692","https://openalex.org/W2115485936","https://openalex.org/W2086064646"],"abstract_inverted_index":{"Chest":[0],"X-Ray":[1],"(CXR)":[2],"images":[3,17],"are":[4],"commonly":[5],"used":[6],"for":[7,15,25,103],"clinical":[8],"screening":[9],"and":[10,29,36,63,79,99,121,139],"diagnosis.":[11],"Automatically":[12],"writing":[13],"reports":[14,41,173],"these":[16],"can":[18],"considerably":[19],"lighten":[20],"the":[21,40,47,52,60,64,95,117,134,164,176],"workload":[22],"of":[23,39,66,73,156],"radiologists":[24],"summarizing":[26],"descriptive":[27],"findings":[28],"conclusive":[30],"impressions.":[31],"The":[32],"complex":[33],"structures":[34],"between":[35,98,119,137],"within":[37,100],"sections":[38,102],"pose":[42],"a":[43,56,90,111,126],"great":[44],"challenge":[45],"to":[46,169],"automatic":[48],"report":[49,101,145],"generation.":[50],"Specifically,":[51],"section":[53,61,70],"Impression":[54],"is":[55,167],"diagnostic":[57],"summarization":[58],"over":[59,71],"Findings;":[62],"appearance":[65],"normality":[67],"dominates":[68],"each":[69],"that":[72,114,131,148,163],"abnormality.":[74],"Existing":[75],"studies":[76],"rarely":[77],"explore":[78],"consider":[80],"this":[81,86],"fundamental":[82],"structure":[83,96,177],"information.":[84,178],"In":[85],"work,":[87],"we":[88,109,124],"propose":[89,110],"novel":[91,127],"framework":[92],"which":[93],"exploits":[94],"information":[97],"generating":[104],"CXR":[105,144],"imaging":[106],"reports.":[107],"First,":[108],"two-stage":[112],"strategy":[113],"explicitly":[115],"models":[116],"relationship":[118],"Findings":[120],"Impression.":[122],"Second,":[123],"design":[125],"cooperative":[128],"multi-agent":[129],"system":[130],"implicitly":[132],"captures":[133],"imbalanced":[135],"distribution":[136],"abnormality":[138],"normality.":[140],"Experiments":[141],"on":[142],"two":[143],"datasets":[146],"show":[147],"our":[149],"method":[150],"achieves":[151],"state-of-the-art":[152],"performance":[153],"in":[154],"terms":[155],"various":[157],"evaluation":[158],"metrics.":[159],"Our":[160],"results":[161],"expose":[162],"proposed":[165],"approach":[166],"able":[168],"generate":[170],"high-quality":[171],"medical":[172],"through":[174],"integrating":[175]},"counts_by_year":[{"year":2026,"cited_by_count":9},{"year":2025,"cited_by_count":35},{"year":2024,"cited_by_count":29},{"year":2023,"cited_by_count":22},{"year":2022,"cited_by_count":19},{"year":2021,"cited_by_count":10}],"updated_date":"2026-07-03T08:13:44.112507","created_date":"2019-06-27T00:00:00"}
