{"id":"https://openalex.org/W2288604516","doi":null,"title":"Optimizing sentence modeling and selection for document summarization","display_name":"Optimizing sentence modeling and selection for document summarization","publication_year":2015,"publication_date":"2015-07-25","ids":{"openalex":"https://openalex.org/W2288604516","mag":"2288604516"},"language":"en","primary_location":{"id":"mag:2288604516","doi":null,"is_oa":false,"landing_page_url":"https://dblp.uni-trier.de/db/conf/ijcai/ijcai2015.html#YinP15","pdf_url":null,"source":{"id":"https://openalex.org/S4306419142","display_name":"International Conference on Artificial Intelligence","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"International Conference on Artificial Intelligence","raw_type":null},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":[],"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/A5038386528","display_name":"Wenpeng Yin","orcid":"https://orcid.org/0000-0002-3154-1139","observed_orcids":["https://orcid.org/0000-0002-3154-1139"]},"institutions":[{"id":"https://openalex.org/I8204097","display_name":"Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen","ror":"https://ror.org/05591te55","country_code":"DE","type":"education","lineage":["https://openalex.org/I8204097"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Wenpeng Yin","raw_affiliation_strings":["The Center for Information and Language Processing, University of Munich#TAB#"],"raw_orcid":"https://orcid.org/0000-0002-3154-1139","affiliations":[{"raw_affiliation_string":"The Center for Information and Language Processing, University of Munich#TAB#","institution_ids":["https://openalex.org/I8204097"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029055424","display_name":"Yulong Pei","orcid":"https://orcid.org/0000-0003-3739-5627","observed_orcids":["https://orcid.org/0000-0003-3739-5627"]},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yulong Pei","raw_affiliation_strings":["School of Computer Science Carnegie Mellon University"],"raw_orcid":"https://orcid.org/0000-0003-3739-5627","affiliations":[{"raw_affiliation_string":"School of Computer Science Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.1914,"has_fulltext":false,"cited_by_count":95,"citation_normalized_percentile":{"value":0.97026299,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1383","last_page":"1389"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.7993000149726868,"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":0.7993000149726868,"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":0.19629999995231628,"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":0.0006000000284984708,"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/extractive-summarization","display_name":"extractive summarization","score":0.8410000205039978},{"id":"https://openalex.org/keywords/sentence-selection","display_name":"sentence selection","score":0.8169999718666077},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.8379999995231628},{"id":"https://openalex.org/keywords/cosine-similarity","display_name":"cosine similarity","score":0.8600000143051147}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8475927114486694},{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.8168677687644958},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.7753560543060303},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6856774091720581},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.6010388731956482},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5959215760231018},{"id":"https://openalex.org/C2780762811","wikidata":"https://www.wikidata.org/wiki/Q1784941","display_name":"Cosine similarity","level":3,"score":0.5843554735183716},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.563572883605957},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5262138843536377},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.49807262420654297},{"id":"https://openalex.org/C134714966","wikidata":"https://www.wikidata.org/wiki/Q6934448","display_name":"Multi-document summarization","level":3,"score":0.47210612893104553},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.4240216016769409},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3281922936439514},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.21081310510635376},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:2288604516","doi":null,"is_oa":false,"landing_page_url":"https://dblp.uni-trier.de/db/conf/ijcai/ijcai2015.html#YinP15","pdf_url":null,"source":{"id":"https://openalex.org/S4306419142","display_name":"International Conference on Artificial Intelligence","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"International Conference on Artificial Intelligence","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W42510783","https://openalex.org/W198356813","https://openalex.org/W1548387548","https://openalex.org/W1614298861","https://openalex.org/W1680189815","https://openalex.org/W1832693441","https://openalex.org/W1854214752","https://openalex.org/W1880262756","https://openalex.org/W1975917757","https://openalex.org/W1998412072","https://openalex.org/W2012179416","https://openalex.org/W2036836182","https://openalex.org/W2038721957","https://openalex.org/W2083778364","https://openalex.org/W2089391273","https://openalex.org/W2102864210","https://openalex.org/W2110693578","https://openalex.org/W2112796928","https://openalex.org/W2120301758","https://openalex.org/W2120615054","https://openalex.org/W2131744502","https://openalex.org/W2138621811","https://openalex.org/W2139153206","https://openalex.org/W2147406357","https://openalex.org/W2154652894","https://openalex.org/W2160774044","https://openalex.org/W2165897980","https://openalex.org/W2168336866","https://openalex.org/W2170292186","https://openalex.org/W2251803266","https://openalex.org/W2952230511"],"related_works":["https://openalex.org/W2896807716","https://openalex.org/W4376140769","https://openalex.org/W2998939484","https://openalex.org/W2083403810","https://openalex.org/W2093119036","https://openalex.org/W4288286156","https://openalex.org/W2992935634","https://openalex.org/W2934929583","https://openalex.org/W2568676725","https://openalex.org/W2579653291"],"abstract_inverted_index":{"Extractive":[0],"document":[1],"summarization":[2],"aims":[3],"to":[4,20,30,39,49,71],"conclude":[5],"given":[6],"documents":[7],"by":[8,45,82],"extracting":[9],"some":[10,106],"salient":[11],"sentences.":[12,35],"Often,":[13],"it":[14,55,86],"faces":[15],"two":[16],"challenges:":[17],"1)":[18],"how":[19,29],"model":[21,63],"the":[22,32,88,102,128],"information":[23],"redundancy":[24,81],"among":[25],"candidate":[26],"sentences;":[27],"2)":[28],"select":[31],"most":[33],"appropriate":[34],"This":[36],"paper":[37],"attempts":[38],"build":[40],"a":[41,58,96],"strong":[42],"summarizer":[43],"DivSelect+CNNLM":[44],"presenting":[46],"new":[47],"algorithms":[48],"optimize":[50],"each":[51,116],"of":[52,104,130],"them.":[53],"Concretely,":[54],"proposes":[56],"CNNLM,":[57],"novel":[59],"neural":[60,68],"network":[61,69],"language":[62],"(NNLM)":[64],"based":[65],"on":[66,120],"convolutional":[67],"(CNN),":[70],"project":[72],"sentences":[73,107],"into":[74],"dense":[75],"distributed":[76],"representations,":[77],"then":[78],"models":[79],"sentence":[80],"cosine":[83],"similarity.":[84],"Afterwards,":[85],"formulates":[87],"selection":[89,98],"process":[90,99],"as":[91],"an":[92],"optimization":[93],"problem,":[94],"constructing":[95],"diversified":[97],"(DivSelect)":[100],"with":[101,115],"aim":[103],"selecting":[105],"which":[108],"have":[109],"high":[110],"prestige,":[111],"meantime,":[112],"are":[113],"dissimilar":[114],"other.":[117],"Experimental":[118],"results":[119],"DUC2002":[121],"and":[122],"DUC2004":[123],"benchmark":[124],"data":[125],"sets":[126],"demonstrate":[127],"effectiveness":[129],"our":[131],"approach.":[132]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":13},{"year":2020,"cited_by_count":19},{"year":2019,"cited_by_count":19},{"year":2018,"cited_by_count":13},{"year":2017,"cited_by_count":12},{"year":2016,"cited_by_count":4}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2016-06-24T00:00:00"}