{"id":"https://openalex.org/W2897164851","doi":"https://doi.org/10.18653/v1/w18-6505","title":"End-to-End Content and Plan Selection for Data-to-Text Generation","display_name":"End-to-End Content and Plan Selection for Data-to-Text Generation","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2897164851","doi":"https://doi.org/10.18653/v1/w18-6505","mag":"2897164851"},"language":"en","primary_location":{"id":"doi:10.18653/v1/w18-6505","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w18-6505","pdf_url":"https://www.aclweb.org/anthology/W18-6505.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 11th International Conference on Natural Language Generation","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/W18-6505.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5026789962","display_name":"Sebastian Gehrmann","orcid":"https://orcid.org/0000-0002-8257-9516"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Sebastian Gehrmann","raw_affiliation_strings":["Harvard SEAS"],"affiliations":[{"raw_affiliation_string":"Harvard SEAS","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042427005","display_name":"Falcon Z. Dai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Falcon Dai","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040570970","display_name":"Henry Elder","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Henry Elder","raw_affiliation_strings":["Harvard SEAS"],"affiliations":[{"raw_affiliation_string":"Harvard SEAS","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062546146","display_name":"Alexander M. Rush","orcid":"https://orcid.org/0000-0002-9900-1606"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alexander Rush","raw_affiliation_strings":["Harvard SEAS"],"affiliations":[{"raw_affiliation_string":"Harvard SEAS","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5026789962"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":8.47,"has_fulltext":true,"cited_by_count":68,"citation_normalized_percentile":{"value":0.98067502,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"46","last_page":"56"},"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":0.9998999834060669,"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/T13629","display_name":"Text Readability and Simplification","score":0.9947999715805054,"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/computer-science","display_name":"Computer science","score":0.8104603290557861},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.664973258972168},{"id":"https://openalex.org/keywords/natural-language-generation","display_name":"Natural language generation","score":0.6083318591117859},{"id":"https://openalex.org/keywords/end-to-end-principle","display_name":"End-to-end principle","score":0.5968387126922607},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.5612028241157532},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5551257729530334},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.528156042098999},{"id":"https://openalex.org/keywords/text-generation","display_name":"Text generation","score":0.5246039628982544},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5098849534988403},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4958237111568451},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.462307333946228},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.3910066485404968},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.38693198561668396},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37864983081817627}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8104603290557861},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.664973258972168},{"id":"https://openalex.org/C2776187449","wikidata":"https://www.wikidata.org/wiki/Q1513879","display_name":"Natural language generation","level":3,"score":0.6083318591117859},{"id":"https://openalex.org/C74296488","wikidata":"https://www.wikidata.org/wiki/Q2527392","display_name":"End-to-end principle","level":2,"score":0.5968387126922607},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.5612028241157532},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5551257729530334},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.528156042098999},{"id":"https://openalex.org/C2985684807","wikidata":"https://www.wikidata.org/wiki/Q1513879","display_name":"Text generation","level":2,"score":0.5246039628982544},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5098849534988403},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4958237111568451},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.462307333946228},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.3910066485404968},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.38693198561668396},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37864983081817627},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/w18-6505","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w18-6505","pdf_url":"https://www.aclweb.org/anthology/W18-6505.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 11th International Conference on Natural Language Generation","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/w18-6505","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w18-6505","pdf_url":"https://www.aclweb.org/anthology/W18-6505.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 11th International Conference on Natural Language Generation","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.8600000143051147}],"awards":[],"funders":[{"id":"https://openalex.org/F4320332195","display_name":"Samsung","ror":"https://ror.org/04w3jy968"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2897164851.pdf","grobid_xml":"https://content.openalex.org/works/W2897164851.grobid-xml"},"referenced_works_count":43,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1902237438","https://openalex.org/W1948566616","https://openalex.org/W1956340063","https://openalex.org/W2004637830","https://openalex.org/W2064675550","https://openalex.org/W2078861931","https://openalex.org/W2099542783","https://openalex.org/W2100175927","https://openalex.org/W2101105183","https://openalex.org/W2110313598","https://openalex.org/W2122514299","https://openalex.org/W2130942839","https://openalex.org/W2133459682","https://openalex.org/W2133564696","https://openalex.org/W2139079654","https://openalex.org/W2148985659","https://openalex.org/W2154652894","https://openalex.org/W2507756961","https://openalex.org/W2525778437","https://openalex.org/W2578330760","https://openalex.org/W2606974598","https://openalex.org/W2612675303","https://openalex.org/W2626515078","https://openalex.org/W2787644385","https://openalex.org/W2804491889","https://openalex.org/W2962729880","https://openalex.org/W2962768052","https://openalex.org/W2962905474","https://openalex.org/W2962944953","https://openalex.org/W2962945412","https://openalex.org/W2963154279","https://openalex.org/W2963212250","https://openalex.org/W2963260202","https://openalex.org/W2963403868","https://openalex.org/W2963910262","https://openalex.org/W2963929190","https://openalex.org/W2964116568","https://openalex.org/W2964121744","https://openalex.org/W2964253663","https://openalex.org/W4300532998","https://openalex.org/W4301504317","https://openalex.org/W4385245566"],"related_works":["https://openalex.org/W4287812620","https://openalex.org/W3019906500","https://openalex.org/W2171759076","https://openalex.org/W4285816270","https://openalex.org/W2573463306","https://openalex.org/W67826833","https://openalex.org/W2955859849","https://openalex.org/W382594479","https://openalex.org/W2153939059","https://openalex.org/W139449664"],"abstract_inverted_index":{"Learning":[0],"to":[1,42,45,71,74],"generate":[2],"fluent":[3],"natural":[4],"language":[5],"from":[6],"structured":[7,29],"data":[8,30],"with":[9],"neural":[10],"networks":[11],"has":[12],"become":[13],"an":[14,88],"common":[15],"approach":[16],"for":[17,47],"NLG.":[18],"This":[19,34],"problem":[20],"can":[21],"be":[22],"challenging":[23],"when":[24],"the":[25,28,48,91],"form":[26],"of":[27,39,55,84,93],"varies":[31],"between":[32],"examples.":[33],"paper":[35],"presents":[36],"a":[37,64],"survey":[38],"several":[40],"extensions":[41],"sequence-to-sequence":[43],"models":[44,73],"account":[46],"latent":[49],"content":[50],"selection":[51],"process,":[52],"particularly":[53],"variants":[54],"copy":[56],"attention":[57],"and":[58],"coverage":[59],"decoding.":[60],"We":[61],"further":[62],"propose":[63],"training":[65],"method":[66],"based":[67],"on":[68],"diverse":[69],"ensembling":[70],"encourage":[72],"learn":[75],"distinct":[76],"sentence":[77],"templates":[78],"during":[79],"training.":[80],"An":[81],"empirical":[82],"evaluation":[83],"these":[85],"techniques":[86],"shows":[87],"increase":[89],"in":[90],"quality":[92],"generated":[94],"text":[95],"across":[96],"five":[97],"automated":[98],"metrics,":[99],"as":[100,102],"well":[101],"human":[103],"evaluation.":[104]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":22},{"year":2020,"cited_by_count":22},{"year":2019,"cited_by_count":8}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}