{"id":"https://openalex.org/W4306317033","doi":"https://doi.org/10.1145/3511808.3557538","title":"A Preliminary Exploration of Extractive Multi-Document Summarization in Hyperbolic Space","display_name":"A Preliminary Exploration of Extractive Multi-Document Summarization in Hyperbolic Space","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4306317033","doi":"https://doi.org/10.1145/3511808.3557538"},"language":"en","primary_location":{"id":"doi:10.1145/3511808.3557538","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3511808.3557538","pdf_url":null,"source":{"id":"https://openalex.org/S4363608762","display_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Information &amp; 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/A5063533156","display_name":"Mingyang Song","orcid":"https://orcid.org/0000-0001-9492-261X"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Mingyang Song","raw_affiliation_strings":["Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100965549","display_name":"Yi Feng","orcid":"https://orcid.org/0000-0002-8358-2995"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Feng","raw_affiliation_strings":["Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069749738","display_name":"Liping Jing","orcid":"https://orcid.org/0000-0001-7578-3407"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liping Jing","raw_affiliation_strings":["Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5063533156"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":0.7276,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.71536307,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"4505","last_page":"4509"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9943000078201294,"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":0.9318188428878784},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7044520974159241},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.6002535820007324},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.5579819679260254},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.5281952619552612},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.49321359395980835},{"id":"https://openalex.org/keywords/syntax","display_name":"Syntax","score":0.46358466148376465},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.4633176326751709},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4444769322872162},{"id":"https://openalex.org/keywords/euclidean-space","display_name":"Euclidean space","score":0.4132801294326782},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3764910399913788},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21960559487342834},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.11308315396308899},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.07921656966209412},{"id":"https://openalex.org/keywords/pure-mathematics","display_name":"Pure mathematics","score":0.07126587629318237}],"concepts":[{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.9318188428878784},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7044520974159241},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.6002535820007324},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.5579819679260254},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.5281952619552612},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.49321359395980835},{"id":"https://openalex.org/C60048249","wikidata":"https://www.wikidata.org/wiki/Q37437","display_name":"Syntax","level":2,"score":0.46358466148376465},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.4633176326751709},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4444769322872162},{"id":"https://openalex.org/C186450821","wikidata":"https://www.wikidata.org/wiki/Q17295","display_name":"Euclidean space","level":2,"score":0.4132801294326782},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3764910399913788},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21960559487342834},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.11308315396308899},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.07921656966209412},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.07126587629318237},{"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3511808.3557538","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3511808.3557538","pdf_url":null,"source":{"id":"https://openalex.org/S4363608762","display_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W2083305840","https://openalex.org/W2889518897","https://openalex.org/W2962892594","https://openalex.org/W2962946054","https://openalex.org/W2997713239","https://openalex.org/W3035113230","https://openalex.org/W4228996975","https://openalex.org/W4294829641"],"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/W402673672","https://openalex.org/W2119384858"],"abstract_inverted_index":{"Summary":[0],"matching":[1],"is":[2,68],"a":[3,91],"recently":[4],"proposed":[5],"paradigm":[6],"for":[7,96,142],"extractive":[8,83,99],"summarization.":[9],"It":[10],"aims":[11],"to":[12,29,70,116],"calculate":[13],"similarities":[14,125,134],"between":[15,48],"candidate":[16,49,105],"summaries":[17,24,50,106],"and":[18,22,42,51,89,107,120],"their":[19,52,108],"corresponding":[20,53,109],"document":[21,54,110],"extract":[23],"by":[25,77,137,161],"ranking":[26],"similarities.":[27,62],"Due":[28],"natural":[30],"languages":[31],"often":[32],"exhibiting":[33],"the":[34,44,60,64,73,78,86,97,113,117,123,127,132,138,144,150,155,164],"inherent":[35],"hierarchical":[36,46],"structures":[37,47],"ingrained":[38],"with":[39,163],"complex":[40],"syntax":[41],"semantics,":[43],"latent":[45],"should":[55],"be":[56],"considered":[57],"when":[58],"calculating":[59],"summary-document":[61,124,133],"However,":[63],"above":[65,79],"structural":[66],"property":[67],"hard":[69],"model":[71,159],"in":[72,85],"Euclidean":[74,114],"space.":[75],"Inspired":[76],"issues,":[80],"we":[81],"explore":[82],"summarization":[84,100],"hyperbolic":[87],"space":[88,115,119],"propose":[90],"new":[92],"Hyperbolic":[93,118],"Siamese":[94],"Network":[95],"matching-based":[98],"(HyperSiameseNet).":[101],"Specifically,":[102],"HyperSiameseNet":[103,160],"projects":[104],"representations":[111],"from":[112],"then":[121],"models":[122],"via":[126],"squared":[128],"poincar\u00e9":[129],"distance.":[130],"Finally,":[131],"are":[135],"optimized":[136],"margin-based":[139],"triplet":[140],"loss":[141],"extracting":[143],"final":[145],"summary.":[146],"The":[147],"results":[148],"on":[149],"Multi-News":[151],"dataset":[152],"have":[153],"shown":[154],"superiority":[156],"of":[157],"our":[158],"comparing":[162],"state-of-the-art":[165],"baselines.":[166]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":6}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
