{"id":"https://openalex.org/W2950161719","doi":"https://doi.org/10.18653/v1/p19-1215","title":"On the Summarization of Consumer Health Questions","display_name":"On the Summarization of Consumer Health Questions","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2950161719","doi":"https://doi.org/10.18653/v1/p19-1215","mag":"2950161719"},"language":"en","primary_location":{"id":"doi:10.18653/v1/p19-1215","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p19-1215","pdf_url":"https://www.aclweb.org/anthology/P19-1215.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":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/P19-1215.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5011349649","display_name":"Asma Ben Abacha","orcid":"https://orcid.org/0000-0001-6312-9387"},"institutions":[{"id":"https://openalex.org/I2800548410","display_name":"United States National Library of Medicine","ror":"https://ror.org/0060t0j89","country_code":"US","type":"archive","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238","https://openalex.org/I2800548410"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Asma Ben Abacha","raw_affiliation_strings":["U.S. National Library of Medicine, Bethesda, MD"],"affiliations":[{"raw_affiliation_string":"U.S. National Library of Medicine, Bethesda, MD","institution_ids":["https://openalex.org/I2800548410"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046764593","display_name":"Dina Demner\u2010Fushman","orcid":null},"institutions":[{"id":"https://openalex.org/I2800548410","display_name":"United States National Library of Medicine","ror":"https://ror.org/0060t0j89","country_code":"US","type":"archive","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238","https://openalex.org/I2800548410"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dina Demner-Fushman","raw_affiliation_strings":["U.S. National Library of Medicine, Bethesda, MD"],"affiliations":[{"raw_affiliation_string":"U.S. National Library of Medicine, Bethesda, MD","institution_ids":["https://openalex.org/I2800548410"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5011349649"],"corresponding_institution_ids":["https://openalex.org/I2800548410"],"apc_list":null,"apc_paid":null,"fwci":3.6127,"has_fulltext":true,"cited_by_count":65,"citation_normalized_percentile":{"value":0.94399785,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"2228","last_page":"2234"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":1.0,"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":1.0,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9977999925613403,"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":0.978600025177002,"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/automatic-summarization","display_name":"Automatic summarization","score":0.9588642120361328},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.836530327796936},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6596935987472534},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6314804553985596},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.604616641998291},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5932579040527344},{"id":"https://openalex.org/keywords/pointer","display_name":"Pointer (user interface)","score":0.5524673461914062},{"id":"https://openalex.org/keywords/false-positive-paradox","display_name":"False positive paradox","score":0.48405593633651733},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.46237775683403015},{"id":"https://openalex.org/keywords/natural-language-generation","display_name":"Natural language generation","score":0.41482940316200256},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.39879050850868225},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37419334053993225}],"concepts":[{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.9588642120361328},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.836530327796936},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6596935987472534},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6314804553985596},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.604616641998291},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5932579040527344},{"id":"https://openalex.org/C150202949","wikidata":"https://www.wikidata.org/wiki/Q107602","display_name":"Pointer (user interface)","level":2,"score":0.5524673461914062},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.48405593633651733},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.46237775683403015},{"id":"https://openalex.org/C2776187449","wikidata":"https://www.wikidata.org/wiki/Q1513879","display_name":"Natural language generation","level":3,"score":0.41482940316200256},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.39879050850868225},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37419334053993225},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/p19-1215","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p19-1215","pdf_url":"https://www.aclweb.org/anthology/P19-1215.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"}],"best_oa_location":{"id":"doi:10.18653/v1/p19-1215","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p19-1215","pdf_url":"https://www.aclweb.org/anthology/P19-1215.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":[{"id":"https://metadata.un.org/sdg/4","score":0.7599999904632568,"display_name":"Quality Education"}],"awards":[{"id":"https://openalex.org/G6101595067","display_name":null,"funder_award_id":"intramura","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"}],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320337372","display_name":"U.S. National Library of Medicine","ror":"https://ror.org/0060t0j89"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2950161719.pdf","grobid_xml":"https://content.openalex.org/works/W2950161719.grobid-xml"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W632432350","https://openalex.org/W1591706642","https://openalex.org/W1886082488","https://openalex.org/W1894439495","https://openalex.org/W2031614194","https://openalex.org/W2072692647","https://openalex.org/W2092433005","https://openalex.org/W2100520226","https://openalex.org/W2133564696","https://openalex.org/W2150824314","https://openalex.org/W2162311237","https://openalex.org/W2280798142","https://openalex.org/W2345785790","https://openalex.org/W2424238744","https://openalex.org/W2467173223","https://openalex.org/W2468484304","https://openalex.org/W2507668380","https://openalex.org/W2582146834","https://openalex.org/W2604748391","https://openalex.org/W2606974598","https://openalex.org/W2774668418","https://openalex.org/W2793567354","https://openalex.org/W2888507208","https://openalex.org/W2889518897","https://openalex.org/W2913352150","https://openalex.org/W2915240437","https://openalex.org/W2962965405","https://openalex.org/W2963260202","https://openalex.org/W2964053384","https://openalex.org/W2964165364","https://openalex.org/W2964308564","https://openalex.org/W3023167149"],"related_works":["https://openalex.org/W2366403280","https://openalex.org/W1495108544","https://openalex.org/W2091301346","https://openalex.org/W3148229873","https://openalex.org/W4389760904","https://openalex.org/W2150160875","https://openalex.org/W4242223894","https://openalex.org/W4214678372","https://openalex.org/W1605559518","https://openalex.org/W4214601164"],"abstract_inverted_index":{"Question":[0],"understanding":[1],"is":[2],"one":[3],"of":[4,34,63,113],"the":[5,32,35,60,95],"main":[6],"challenges":[7],"in":[8,43],"question":[9,56,92,131],"answering.":[10],"In":[11,46,84],"real":[12],"world":[13],"applications,":[14],"users":[15],"often":[16],"submit":[17],"natural":[18],"language":[19],"questions":[20],"that":[21,30,88,99,127],"are":[22,128],"longer":[23],"than":[24],"needed":[25],"and":[26,74,98,122],"include":[27],"peripheral":[28],"information":[29],"increases":[31],"complexity":[33],"question,":[36],"leading":[37],"to":[38,130],"substantially":[39],"more":[40],"false":[41],"positives":[42],"answer":[44],"retrieval.":[45],"this":[47,81,107],"paper,":[48],"we":[49,86],"study":[50],"neural":[51,77],"abstractive":[52,78],"models":[53,79,105],"for":[54,125],"medical":[55],"summarization.":[57,132],"We":[58,69,115],"introduce":[59],"MeQSum":[61],"corpus":[62],"1,000":[64],"summarized":[65],"consumer":[66],"health":[67],"questions.":[68],"explore":[70],"data":[71],"augmentation":[72,90],"methods":[73],"evaluate":[75],"state-of-the-art":[76],"on":[80,106],"new":[82],"task.":[83],"particular,":[85],"show":[87],"semantic":[89],"from":[91],"datasets":[93],"improves":[94],"overall":[96],"performance,":[97],"pointer-generator":[100],"networks":[101],"outperform":[102],"sequence-to-sequence":[103],"attentional":[104],"task,":[108],"with":[109],"a":[110,118],"ROUGE-1":[111],"score":[112],"44.16%.":[114],"also":[116],"present":[117],"detailed":[119],"error":[120],"analysis":[121],"discuss":[123],"directions":[124],"improvement":[126],"specific":[129]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":15},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":14},{"year":2020,"cited_by_count":2}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
