{"id":"https://openalex.org/W2896807716","doi":"https://doi.org/10.18653/v1/p18-1061","title":"Neural Document Summarization by Jointly Learning to Score and Select Sentences","display_name":"Neural Document Summarization by Jointly Learning to Score and Select Sentences","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2896807716","doi":"https://doi.org/10.18653/v1/p18-1061","mag":"2896807716"},"language":"en","primary_location":{"id":"doi:10.18653/v1/p18-1061","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p18-1061","pdf_url":"https://www.aclweb.org/anthology/P18-1061.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 56th 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://www.aclweb.org/anthology/P18-1061.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5052503472","display_name":"Qingyu Zhou","orcid":"https://orcid.org/0000-0002-4389-1582"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingyu Zhou","raw_affiliation_strings":["Harbin Institute of Technology, Harbin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072808499","display_name":"Nan Yang","orcid":"https://orcid.org/0000-0002-2621-8927"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Nan Yang","raw_affiliation_strings":["Microsoft Research, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Research, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014662947","display_name":"Furu Wei","orcid":"https://orcid.org/0000-0002-7810-5852"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Furu Wei","raw_affiliation_strings":["Microsoft Research, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Research, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061624006","display_name":"Shaohan Huang","orcid":"https://orcid.org/0000-0003-4324-6337"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaohan Huang","raw_affiliation_strings":["Microsoft Research, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Research, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100701572","display_name":"Ming Zhou","orcid":"https://orcid.org/0000-0002-2551-2964"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ming Zhou","raw_affiliation_strings":["Microsoft Research, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Research, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101661008","display_name":"Tiejun Zhao","orcid":"https://orcid.org/0000-0003-4659-4935"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tiejun Zhao","raw_affiliation_strings":["Harbin Institute of Technology, Harbin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":359,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"654","last_page":"663"},"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":0.9998999834060669,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.996999979019165,"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":0.9125790596008301},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8531951308250427},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.7246724367141724},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7057321667671204},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.7016503810882568},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6208506226539612},{"id":"https://openalex.org/keywords/multi-document-summarization","display_name":"Multi-document summarization","score":0.5857768058776855},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4990673065185547},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.4923819303512573},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.48526832461357117},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4689708948135376},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33665215969085693}],"concepts":[{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.9125790596008301},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8531951308250427},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.7246724367141724},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7057321667671204},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.7016503810882568},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6208506226539612},{"id":"https://openalex.org/C134714966","wikidata":"https://www.wikidata.org/wiki/Q6934448","display_name":"Multi-document summarization","level":3,"score":0.5857768058776855},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4990673065185547},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.4923819303512573},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.48526832461357117},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4689708948135376},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33665215969085693},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","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/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/p18-1061","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p18-1061","pdf_url":"https://www.aclweb.org/anthology/P18-1061.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 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/p18-1061","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p18-1061","pdf_url":"https://www.aclweb.org/anthology/P18-1061.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 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.4300000071525574}],"awards":[{"id":"https://openalex.org/G1712057025","display_name":null,"funder_award_id":"91520204","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2915035755","display_name":null,"funder_award_id":"2017YFB1002102","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321940","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2896807716.pdf","grobid_xml":"https://content.openalex.org/works/W2896807716.grobid-xml"},"referenced_works_count":37,"referenced_works":["https://openalex.org/W168564468","https://openalex.org/W912777836","https://openalex.org/W1522301498","https://openalex.org/W1525595230","https://openalex.org/W1533861849","https://openalex.org/W1544827683","https://openalex.org/W1815076433","https://openalex.org/W1821462560","https://openalex.org/W1973894278","https://openalex.org/W1974339500","https://openalex.org/W2054211469","https://openalex.org/W2083305840","https://openalex.org/W2095705004","https://openalex.org/W2101390659","https://openalex.org/W2123086176","https://openalex.org/W2144933361","https://openalex.org/W2150869743","https://openalex.org/W2152992673","https://openalex.org/W2154652894","https://openalex.org/W2157331557","https://openalex.org/W2250539671","https://openalex.org/W2251911042","https://openalex.org/W2293771131","https://openalex.org/W2307381258","https://openalex.org/W2549416390","https://openalex.org/W2573170368","https://openalex.org/W2574535369","https://openalex.org/W2606974598","https://openalex.org/W2735674392","https://openalex.org/W2899771611","https://openalex.org/W2949615363","https://openalex.org/W2952138241","https://openalex.org/W2962964385","https://openalex.org/W2963929190","https://openalex.org/W2964121744","https://openalex.org/W3101913037","https://openalex.org/W3138773240"],"related_works":["https://openalex.org/W2104677027","https://openalex.org/W3164984162","https://openalex.org/W2902627734","https://openalex.org/W2112885393","https://openalex.org/W1990695371","https://openalex.org/W2173208124","https://openalex.org/W2568827738","https://openalex.org/W2099859325","https://openalex.org/W2365100044","https://openalex.org/W2474342320"],"abstract_inverted_index":{"Sentence":[0],"scoring":[1,86],"and":[2,43],"sentence":[3],"selection":[4,82],"are":[5],"two":[6,20],"main":[7],"steps":[8],"in":[9],"extractive":[10,35,113],"document":[11,36,50],"summarization":[12,37,114],"systems.":[13],"However,":[14],"previous":[15,76],"works":[16],"treat":[17],"them":[18],"as":[19],"separated":[21],"subtasks.":[22],"In":[23],"this":[24],"paper,":[25],"we":[26],"present":[27],"a":[28,53],"novel":[29],"end-to-end":[30],"neural":[31],"network":[32],"framework":[33,108],"for":[34],"by":[38,68,72],"jointly":[39],"learning":[40],"to":[41,56],"score":[42],"select":[44],"sentences.":[45,61,97],"It":[46],"first":[47],"reads":[48],"the":[49,58,65,81,85,91,100,106,111],"sentences":[51,70],"with":[52],"hierarchical":[54],"encoder":[55],"obtain":[57],"representation":[59],"of":[60],"Then":[62],"it":[63],"builds":[64],"output":[66],"summary":[67],"extracting":[69],"one":[71],"one.":[73],"Different":[74],"from":[75],"methods,":[77],"our":[78],"approach":[79],"integrates":[80],"strategy":[83],"into":[84],"model,":[87],"which":[88],"directly":[89],"predicts":[90],"relative":[92],"importance":[93],"given":[94],"previously":[95],"selected":[96],"Experiments":[98],"on":[99],"CNN/Daily":[101],"Mail":[102],"dataset":[103],"show":[104],"that":[105],"proposed":[107],"significantly":[109],"outperforms":[110],"state-of-the-art":[112],"models.":[115]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":28},{"year":2023,"cited_by_count":45},{"year":2022,"cited_by_count":45},{"year":2021,"cited_by_count":74},{"year":2020,"cited_by_count":87},{"year":2019,"cited_by_count":64},{"year":2018,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
