{"id":"https://openalex.org/W4412889776","doi":"https://doi.org/10.18653/v1/2025.acl-long.1398","title":"ERU-KG: Efficient Reference-aligned Unsupervised Keyphrase Generation","display_name":"ERU-KG: Efficient Reference-aligned Unsupervised Keyphrase Generation","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4412889776","doi":"https://doi.org/10.18653/v1/2025.acl-long.1398"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2025.acl-long.1398","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.acl-long.1398","pdf_url":"https://aclanthology.org/2025.acl-long.1398.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 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2025.acl-long.1398.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5004743631","display_name":"Lam Thanh","orcid":"https://orcid.org/0000-0002-1854-4888"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lam Thanh Do","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Aaditya Bodke","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aaditya Bodke","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061048925","display_name":"Pritom Saha Akash","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pritom Saha Akash","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5101880377","display_name":"Kevin Chen\u2013Chuan Chang","orcid":"https://orcid.org/0000-0003-0997-6803"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kevin Chen-Chuan Chang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.14737812,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"28811","last_page":"28829"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9976999759674072,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9976999759674072,"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":0.6777827739715576},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45716679096221924}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6777827739715576},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45716679096221924}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2025.acl-long.1398","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.acl-long.1398","pdf_url":"https://aclanthology.org/2025.acl-long.1398.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 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2025.acl-long.1398","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.acl-long.1398","pdf_url":"https://aclanthology.org/2025.acl-long.1398.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 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3668672548","display_name":null,"funder_award_id":"083650","funder_id":"https://openalex.org/F4320322927","funder_display_name":"Zhejiang University"},{"id":"https://openalex.org/G7304527097","display_name":null,"funder_award_id":"IIS 16-33755","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7716327847","display_name":null,"funder_award_id":"IIS 16-19302","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320316786","display_name":"Center for Cognitive Computing Systems Research","ror":null},{"id":"https://openalex.org/F4320322927","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884"},{"id":"https://openalex.org/F4320332222","display_name":"University of Illinois at Urbana-Champaign","ror":"https://ror.org/047426m28"},{"id":"https://openalex.org/F4320337691","display_name":"Office of the Vice Chancellor for Research, University of Illinois at Chicago","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412889776.pdf","grobid_xml":"https://content.openalex.org/works/W4412889776.grobid-xml"},"referenced_works_count":1,"referenced_works":["https://openalex.org/W4361193725"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Unsupervised":[0],"keyphrase":[1,41,59,126,199],"prediction":[2],"has":[3],"gained":[4],"growing":[5],"interest":[6],"in":[7,98],"recent":[8],"years.However,":[9],"existing":[10],"methods":[11],"typically":[12],"rely":[13],"on":[14,125,135,158],"heuristically":[15],"defined":[16],"importance":[17],"scores,":[18],"which":[19],"may":[20],"lead":[21],"to":[22,72,149,207],"inaccurate":[23],"informativeness":[24,49,67,74,104],"estimation.In":[25],"addition,":[26],"they":[27],"lack":[28],"consideration":[29],"for":[30,116,145],"time":[31],"efficiency.To":[32],"solve":[33],"these":[34],"problems,":[35],"we":[36,154],"propose":[37],"ERU-KG,":[38],"an":[39,48],"unsupervised":[40,131],"generation":[42,127,200],"(UKG)":[43],"model":[44,73,144,157,191,195],"that":[45,164,182],"consists":[46],"of":[47,58,94,105,119,138,141,189],"and":[50,81,83,101,133,162,175,201],"a":[51,142],"phraseness":[52],"module.The":[53],"former":[54],"estimates":[55],"the":[56,62,85,91,114,120,139,156,185],"relevance":[57],"candidates,":[60],"while":[61],"latter":[63],"generate":[64],"those":[65],"candidates.The":[66],"module":[68],"innovates":[69],"by":[70,109,129,167,203],"learning":[71],"through":[75],"references":[76],"(e.g.,":[77],"queries,":[78],"citation":[79],"contexts,":[80],"titles)":[82],"at":[84],"term-level,":[86],"thereby":[87],"1)":[88],"capturing":[89],"how":[90],"key":[92],"concepts":[93],"documents":[95],"are":[96,169],"perceived":[97],"different":[99],"contexts":[100],"2)":[102],"estimating":[103],"phrases":[106],"more":[107],"efficiently":[108],"aggregating":[110],"term":[111],"informativeness,":[112],"removing":[113],"need":[115],"explicit":[117],"modeling":[118],"candidates.ERU-KG":[121],"demonstrates":[122],"its":[123,151],"effectiveness":[124],"benchmarks":[128],"outperforming":[130],"baselines":[132,188],"achieving":[134],"average":[136],"89%":[137],"performance":[140],"supervised":[143],"top":[146],"10":[147],"predictions.Additionally,":[148],"highlight":[150],"practical":[152],"utility,":[153],"evaluate":[155],"text":[159],"retrieval":[160],"tasks":[161],"show":[163],"keyphrases":[165],"generated":[166],"ERU-KG":[168,183],"effective":[170],"when":[171],"employed":[172],"as":[173],"query":[174],"document":[176],"expansions.Furthermore,":[177],"inference":[178],"speed":[179],"tests":[180],"reveal":[181],"is":[184],"fastest":[186],"among":[187],"similar":[190],"sizes.Finally,":[192],"our":[193],"proposed":[194],"can":[196],"switch":[197],"between":[198],"extraction":[202],"adjusting":[204],"hyperparameters,":[205],"catering":[206],"diverse":[208],"application":[209],"requirements.":[210],"1":[211]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-08-04T00:00:00"}