{"id":"https://openalex.org/W3211144921","doi":"https://doi.org/10.1145/3459637.3482066","title":"Boosting Few-shot Abstractive Summarization with Auxiliary Tasks","display_name":"Boosting Few-shot Abstractive Summarization with Auxiliary Tasks","publication_year":2021,"publication_date":"2021-10-26","ids":{"openalex":"https://openalex.org/W3211144921","doi":"https://doi.org/10.1145/3459637.3482066","mag":"3211144921"},"language":"en","primary_location":{"id":"doi:10.1145/3459637.3482066","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3459637.3482066","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","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/A5024176301","display_name":"Qiwei Bi","orcid":null},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qiwei Bi","raw_affiliation_strings":["Renmin University of China, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Renmin University of China, Beijing, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100673440","display_name":"Haoyuan Li","orcid":"https://orcid.org/0009-0007-4678-9923"},"institutions":[{"id":"https://openalex.org/I136199984","display_name":"Harvard University","ror":"https://ror.org/03vek6s52","country_code":"US","type":"education","lineage":["https://openalex.org/I136199984"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haoyuan Li","raw_affiliation_strings":["Harvard University, Boston, MA, USA"],"affiliations":[{"raw_affiliation_string":"Harvard University, Boston, MA, USA","institution_ids":["https://openalex.org/I136199984"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084917712","display_name":"Hanfang Yang","orcid":"https://orcid.org/0000-0002-0983-6758"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hanfang Yang","raw_affiliation_strings":["Renmin University of China, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Renmin University of China, Beijing, China","institution_ids":["https://openalex.org/I78988378"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5024176301"],"corresponding_institution_ids":["https://openalex.org/I78988378"],"apc_list":null,"apc_paid":null,"fwci":0.136,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.56335346,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"2888","last_page":"2893"},"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":0.9997000098228455,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9940000176429749,"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":0.9056540727615356},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8745200634002686},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6146081686019897},{"id":"https://openalex.org/keywords/adapter","display_name":"Adapter (computing)","score":0.5910991430282593},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5531090497970581},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.5082323551177979},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.450503408908844},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40013229846954346}],"concepts":[{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.9056540727615356},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8745200634002686},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6146081686019897},{"id":"https://openalex.org/C177284502","wikidata":"https://www.wikidata.org/wiki/Q1005390","display_name":"Adapter (computing)","level":2,"score":0.5910991430282593},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5531090497970581},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5082323551177979},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.450503408908844},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40013229846954346},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3459637.3482066","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3459637.3482066","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8299999833106995,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[{"id":"https://openalex.org/G3513754391","display_name":null,"funder_award_id":"2000302","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1490960179","https://openalex.org/W2123442489","https://openalex.org/W2757311323","https://openalex.org/W2798935874","https://openalex.org/W2882999943","https://openalex.org/W2892181857","https://openalex.org/W2913991091","https://openalex.org/W2933138175","https://openalex.org/W2950300355","https://openalex.org/W2950342809","https://openalex.org/W2962849707","https://openalex.org/W2963929190","https://openalex.org/W2981852735","https://openalex.org/W3034942609","https://openalex.org/W3034961030","https://openalex.org/W3034999214","https://openalex.org/W3035693354","https://openalex.org/W3106234277","https://openalex.org/W3153675281","https://openalex.org/W3170305303","https://openalex.org/W4239019441","https://openalex.org/W4285719527","https://openalex.org/W4288089799","https://openalex.org/W6600002382"],"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/W4306886878","https://openalex.org/W2973759123","https://openalex.org/W1517524280"],"abstract_inverted_index":{"For":[0],"summarization":[1],"in":[2,60,121],"niche":[3],"domains,":[4],"data":[5],"is":[6],"not":[7],"enough":[8],"to":[9,17,27,74,85,92,109],"fine-tune":[10],"the":[11,19,29,40,46,52,58,61,71,81,87,93,98,112,118,127],"large":[12],"pre-trained":[13],"model.":[14],"In":[15,33],"order":[16],"alleviate":[18],"few-shot":[20,104],"problem,":[21],"we":[22,36,69],"design":[23],"several":[24],"auxiliary":[25,49,90],"tasks":[26,50,59,79,91],"assist":[28],"main":[30,47,94],"task---abstractive":[31],"summarization.":[32],"this":[34,122],"paper,":[35],"employ":[37],"BART":[38],"as":[39],"base":[41],"sequence-to-sequence":[42],"model":[43,113,128],"and":[44,48,80,117],"incorporate":[45],"under":[51],"multi-task":[53],"framework.":[54],"We":[55,106],"transform":[56],"all":[57],"format":[62],"of":[63,89,100],"machine":[64],"reading":[65],"comprehension":[66],"[19].":[67],"Moreover,":[68],"utilize":[70],"task-specific":[72],"adapter":[73],"effectively":[75],"share":[76],"knowledge":[77],"across":[78],"adaptive":[82],"weight":[83],"mechanism":[84],"adjust":[86],"contribution":[88],"task.":[95],"Experiments":[96],"show":[97],"effectiveness":[99],"our":[101],"method":[102],"for":[103],"datasets.":[105],"also":[107],"propose":[108],"firstly":[110],"pre-train":[111],"on":[114],"unlabeled":[115],"datasets,":[116],"methods":[119],"proposed":[120],"paper":[123],"can":[124],"further":[125],"improve":[126],"performance.":[129]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}