{"id":"https://openalex.org/W2948120406","doi":"https://doi.org/10.18653/v1/p19-1102","title":"Multi-News: A Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model","display_name":"Multi-News: A Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2948120406","doi":"https://doi.org/10.18653/v1/p19-1102","mag":"2948120406"},"language":"en","primary_location":{"id":"doi:10.18653/v1/p19-1102","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p19-1102","pdf_url":"https://aclanthology.org/P19-1102.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":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/P19-1102.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5023648764","display_name":"Alexander R. Fabbri","orcid":"https://orcid.org/0000-0002-4670-704X"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alexander Fabbri","raw_affiliation_strings":["Department of Computer Science Yale University","Yale University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science Yale University","institution_ids":[]},{"raw_affiliation_string":"Yale University","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101537931","display_name":"Irene Li","orcid":"https://orcid.org/0000-0002-1851-5390"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Irene Li","raw_affiliation_strings":["Department of Computer Science Yale University","Yale University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science Yale University","institution_ids":[]},{"raw_affiliation_string":"Yale University","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012999357","display_name":"Tianwei She","orcid":null},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tianwei She","raw_affiliation_strings":["Department of Computer Science Yale University","Yale University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science Yale University","institution_ids":[]},{"raw_affiliation_string":"Yale University","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042738110","display_name":"Suyi Li","orcid":"https://orcid.org/0000-0002-0355-1655"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Suyi Li","raw_affiliation_strings":["Department of Computer Science Yale University","Yale University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science Yale University","institution_ids":[]},{"raw_affiliation_string":"Yale University","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081787254","display_name":"Dragomir Radev","orcid":"https://orcid.org/0000-0001-7830-6489"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dragomir Radev","raw_affiliation_strings":["Department of Computer Science Yale University","Yale University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science Yale University","institution_ids":[]},{"raw_affiliation_string":"Yale University","institution_ids":["https://openalex.org/I32971472"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I32971472"],"apc_list":null,"apc_paid":null,"fwci":2.8426,"has_fulltext":true,"cited_by_count":36,"citation_normalized_percentile":{"value":0.93295852,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1074","last_page":"1084"},"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":0.9998000264167786,"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":0.9994000196456909,"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":0.9679742455482483},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8593124151229858},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.746898889541626},{"id":"https://openalex.org/keywords/multi-document-summarization","display_name":"Multi-document summarization","score":0.6989031434059143},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.5992973446846008},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5578189492225647},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5116677284240723},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4436868131160736},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38377031683921814},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3557821214199066}],"concepts":[{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.9679742455482483},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8593124151229858},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.746898889541626},{"id":"https://openalex.org/C134714966","wikidata":"https://www.wikidata.org/wiki/Q6934448","display_name":"Multi-document summarization","level":3,"score":0.6989031434059143},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.5992973446846008},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5578189492225647},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5116677284240723},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4436868131160736},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38377031683921814},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3557821214199066},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.18653/v1/p19-1102","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p19-1102","pdf_url":"https://aclanthology.org/P19-1102.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"},{"id":"pmh:oai:arXiv.org:1906.01749","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1906.01749","pdf_url":"https://arxiv.org/pdf/1906.01749","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:2948120406","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1906.01749","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1906.01749","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1906.01749","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.18653/v1/p19-1102","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p19-1102","pdf_url":"https://aclanthology.org/P19-1102.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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2948120406.pdf","grobid_xml":"https://content.openalex.org/works/W2948120406.grobid-xml"},"referenced_works_count":36,"referenced_works":["https://openalex.org/W1497212108","https://openalex.org/W1525595230","https://openalex.org/W1843891098","https://openalex.org/W1939882552","https://openalex.org/W1975579663","https://openalex.org/W2051618067","https://openalex.org/W2083305840","https://openalex.org/W2118733980","https://openalex.org/W2128672521","https://openalex.org/W2250829779","https://openalex.org/W2270627573","https://openalex.org/W2293778248","https://openalex.org/W2467173223","https://openalex.org/W2579653291","https://openalex.org/W2606974598","https://openalex.org/W2612675303","https://openalex.org/W2626778328","https://openalex.org/W2662348822","https://openalex.org/W2785076661","https://openalex.org/W2794945088","https://openalex.org/W2798240283","https://openalex.org/W2799044970","https://openalex.org/W2888482885","https://openalex.org/W2896919131","https://openalex.org/W2902706465","https://openalex.org/W2906625520","https://openalex.org/W2949615363","https://openalex.org/W2951603207","https://openalex.org/W2951777553","https://openalex.org/W2962849707","https://openalex.org/W2962946054","https://openalex.org/W2962972512","https://openalex.org/W2963045354","https://openalex.org/W2963047186","https://openalex.org/W2963631907","https://openalex.org/W2964308564"],"related_works":["https://openalex.org/W2963204221","https://openalex.org/W3101913037","https://openalex.org/W3104404235","https://openalex.org/W2154652894","https://openalex.org/W1525595230","https://openalex.org/W58306768","https://openalex.org/W3197149826","https://openalex.org/W2902706465","https://openalex.org/W3006471767","https://openalex.org/W2799044970","https://openalex.org/W3102244666","https://openalex.org/W3202129505","https://openalex.org/W2757311323","https://openalex.org/W2613134896","https://openalex.org/W3014920262","https://openalex.org/W3214472221","https://openalex.org/W2798821806","https://openalex.org/W2896457183","https://openalex.org/W2606974598","https://openalex.org/W1988543050"],"abstract_inverted_index":{"Automatic":[0],"generation":[1],"of":[2,15,37,44,52,55],"summaries":[3],"from":[4,27],"multiple":[5],"news":[6,45,68],"articles":[7,46],"is":[8],"a":[9,53,78,84],"valuable":[10],"tool":[11],"as":[12],"the":[13,35,64,118],"number":[14],"online":[16],"publications":[17],"grows":[18],"rapidly.":[19],"Single":[20],"document":[21],"summarization":[22,42,81,116],"(SDS)":[23],"systems":[24],"have":[25],"benefited":[26],"advances":[28,114],"in":[29,107,115,117],"neural":[30],"encoder-decoder":[31],"model":[32,75,82,87],"thanks":[33],"to":[34,50],"availability":[36],"large":[38],"datasets.":[39,94],"However,":[40],"multidocument":[41,119],"(MDS)":[43],"has":[47],"been":[48],"limited":[49],"datasets":[51],"couple":[54],"hundred":[56],"examples.":[57],"In":[58],"this":[59,110],"paper,":[60],"we":[61,71],"introduce":[62],"Multi-News,":[63],"first":[65],"large-scale":[66],"MDS":[67,93],"dataset.":[69],"Additionally,":[70],"propose":[72],"an":[73],"end-to-end":[74],"which":[76],"incorporates":[77],"traditional":[79],"extractive":[80],"with":[83],"standard":[85],"SDS":[86],"and":[88,101,105],"achieves":[89],"competitive":[90],"results":[91],"on":[92,99],"We":[95],"benchmark":[96],"several":[97],"methods":[98],"Multi-News":[100],"release":[102],"our":[103],"data":[104],"code":[106],"hope":[108],"that":[109],"work":[111],"will":[112],"promote":[113],"setting":[120],"1":[121],".":[122]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":14},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":1}],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2025-10-10T00:00:00"}