{"id":"https://openalex.org/W7126388545","doi":"https://doi.org/10.18653/v1/2024.findings-eacl.96","title":"Aspect-based Key Point Analysis for Quantitative Summarization of Reviews","display_name":"Aspect-based Key Point Analysis for Quantitative Summarization of Reviews","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W7126388545","doi":"https://doi.org/10.18653/v1/2024.findings-eacl.96"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2024.findings-eacl.96","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2024.findings-eacl.96","pdf_url":"https://aclanthology.org/2024.findings-eacl.96.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":"Findings of the Association for Computational Linguistics: EACL 2024","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2024.findings-eacl.96.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5108986586","display_name":"An Quang Tang","orcid":null},"institutions":[{"id":"https://openalex.org/I82951845","display_name":"RMIT University","ror":"https://ror.org/04ttjf776","country_code":"AU","type":"education","lineage":["https://openalex.org/I82951845"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"An Quang Tang","raw_affiliation_strings":["RMIT University , Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RMIT University , Australia","institution_ids":["https://openalex.org/I82951845"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124504678","display_name":"Xiuzhen Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I82951845","display_name":"RMIT University","ror":"https://ror.org/04ttjf776","country_code":"AU","type":"education","lineage":["https://openalex.org/I82951845"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Xiuzhen Zhang","raw_affiliation_strings":["RMIT University , Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RMIT University , Australia","institution_ids":["https://openalex.org/I82951845"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006961965","display_name":"Minh Ngoc Dinh","orcid":"https://orcid.org/0000-0003-4026-6068"},"institutions":[{"id":"https://openalex.org/I82951845","display_name":"RMIT University","ror":"https://ror.org/04ttjf776","country_code":"AU","type":"education","lineage":["https://openalex.org/I82951845"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Minh Ngoc Dinh","raw_affiliation_strings":["RMIT University , Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RMIT University , Australia","institution_ids":["https://openalex.org/I82951845"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I82951845"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.56949456,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1419","last_page":"1433"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.5184000134468079,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.5184000134468079,"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":0.06239999830722809,"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":0.05900000035762787,"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":0.6345000267028809},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4968999922275543},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.42739999294281006},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.28290000557899475},{"id":"https://openalex.org/keywords/quantitative-analysis","display_name":"Quantitative analysis (chemistry)","score":0.2556000053882599}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6567999720573425},{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.6345000267028809},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4968999922275543},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4392000138759613},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.42739999294281006},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3472000062465668},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.33559998869895935},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.28290000557899475},{"id":"https://openalex.org/C95986675","wikidata":"https://www.wikidata.org/wiki/Q185168","display_name":"Quantitative analysis (chemistry)","level":2,"score":0.2556000053882599},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.2361000031232834}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2024.findings-eacl.96","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2024.findings-eacl.96","pdf_url":"https://aclanthology.org/2024.findings-eacl.96.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":"Findings of the Association for Computational Linguistics: EACL 2024","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2024.findings-eacl.96","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2024.findings-eacl.96","pdf_url":"https://aclanthology.org/2024.findings-eacl.96.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":"Findings of the Association for Computational Linguistics: EACL 2024","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7126388545.pdf","grobid_xml":"https://content.openalex.org/works/W7126388545.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Key":[0,90],"Point":[1,91],"Analysis":[2,92],"(KPA)":[3],"is":[4],"originally":[5],"for":[6,23,97,116],"summarizing":[7],"arguments,":[8],"where":[9],"short":[10],"sentences":[11],"containing":[12],"salient":[13],"viewpoints":[14],"are":[15],"extracted":[16],"as":[17,26],"key":[18],"points":[19],"(KPs)":[20],"and":[21,44,64,132,147],"quantified":[22],"their":[24],"prevalence":[25],"salience":[27,78],"scores.Recently,":[28],"KPA":[29],"was":[30],"applied":[31],"to":[32,48,69,111,126,130,150],"summarize":[33],"reviews,":[34],"but":[35],"the":[36,61,81,101,136,139,160],"study":[37],"still":[38],"relies":[39],"on":[40,60,155],"sentence-based":[41,51],"KP":[42,68,143],"extraction":[43,52],"matching,":[45],"which":[46],"leads":[47],"two":[49],"issues:":[50],"can":[53,72],"result":[54],"in":[55,76,84],"KPs":[56,129,134],"of":[57,67,109,144,159],"overlapping":[58],"opinions":[59],"same":[62],"aspects,":[63],"sentencebased":[65],"matching":[66,117],"review":[70,99,163],"comment":[71],"be":[73],"inaccurate,":[74],"resulting":[75],"inaccurate":[77],"scores.To":[79],"address":[80],"above":[82],"issues,":[83],"this":[85],"paper,":[86],"we":[87,120],"propose":[88,121],"Aspect-based":[89],"(ABKPA),":[93],"a":[94,122],"novel":[95],"framework":[96,140],"quantitative":[98],"summarization.Leveraging":[100],"readily":[102],"available":[103],"aspect-based":[104],"sentiment":[105],"analysis":[106],"(ABSA)":[107],"resources":[108],"reviews":[110,131],"automatically":[112],"annotate":[113],"silver":[114],"labels":[115],"aspect-sentiment":[118],"pairs,":[119],"contrastive":[123],"learning":[124],"model":[125],"effectively":[127],"match":[128],"quantify":[133],"at":[135],"aspect":[137],"level.Especially,":[138],"ensures":[141],"extracting":[142],"distinct":[145],"aspects":[146],"opinions,":[148],"leading":[149],"more":[151],"accurate":[152],"opinion":[153],"quantification.Experiments":[154],"five":[156],"business":[157],"categories":[158],"popular":[161],"Yelp":[162],"dataset":[164],"show":[165],"that":[166],"ABKPA":[167],"outperforms":[168],"state-of-the-art":[169],"baselines.":[170]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2026-02-02T00:00:00"}