{"id":"https://openalex.org/W3162895869","doi":"https://doi.org/10.1145/3411763.3443441","title":"Towards Explainable AI: Assessing the Usefulness and Impact of Added Explainability Features in Legal Document Summarization","display_name":"Towards Explainable AI: Assessing the Usefulness and Impact of Added Explainability Features in Legal Document Summarization","publication_year":2021,"publication_date":"2021-05-08","ids":{"openalex":"https://openalex.org/W3162895869","doi":"https://doi.org/10.1145/3411763.3443441","mag":"3162895869"},"language":"en","primary_location":{"id":"doi:10.1145/3411763.3443441","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3411763.3443441","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems","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/A5042650113","display_name":"Milda Norkut\u0117","orcid":"https://orcid.org/0000-0002-7817-1171"},"institutions":[{"id":"https://openalex.org/I4210121390","display_name":"Thomson Reuters (Switzerland)","ror":"https://ror.org/02kh7ez55","country_code":"CH","type":"company","lineage":["https://openalex.org/I4210121390","https://openalex.org/I68384125"]}],"countries":["CH"],"is_corresponding":true,"raw_author_name":"Milda Norkute","raw_affiliation_strings":["Thomson Reuters Labs, Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Thomson Reuters Labs, Switzerland","institution_ids":["https://openalex.org/I4210121390"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064710079","display_name":"Nadja Herger","orcid":"https://orcid.org/0000-0002-7971-9020"},"institutions":[{"id":"https://openalex.org/I4210121390","display_name":"Thomson Reuters (Switzerland)","ror":"https://ror.org/02kh7ez55","country_code":"CH","type":"company","lineage":["https://openalex.org/I4210121390","https://openalex.org/I68384125"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Nadja Herger","raw_affiliation_strings":["Thomson Reuters Labs, Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Thomson Reuters Labs, Switzerland","institution_ids":["https://openalex.org/I4210121390"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053883711","display_name":"Leszek Michalak","orcid":"https://orcid.org/0000-0002-9423-083X"},"institutions":[{"id":"https://openalex.org/I4210121390","display_name":"Thomson Reuters (Switzerland)","ror":"https://ror.org/02kh7ez55","country_code":"CH","type":"company","lineage":["https://openalex.org/I4210121390","https://openalex.org/I68384125"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Leszek Michalak","raw_affiliation_strings":["Thomson Reuters Labs, Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Thomson Reuters Labs, Switzerland","institution_ids":["https://openalex.org/I4210121390"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034097416","display_name":"Andrew T. Mulder","orcid":"https://orcid.org/0000-0001-5573-5555"},"institutions":[{"id":"https://openalex.org/I68384125","display_name":"Thomson Reuters (United States)","ror":"https://ror.org/00m7gt169","country_code":"US","type":"company","lineage":["https://openalex.org/I68384125"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Andrew Mulder","raw_affiliation_strings":["Thomson Reuters Labs, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Thomson Reuters Labs, United States","institution_ids":["https://openalex.org/I68384125"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061986206","display_name":"Sally Gao","orcid":"https://orcid.org/0009-0005-5727-0102"},"institutions":[{"id":"https://openalex.org/I68384125","display_name":"Thomson Reuters (United States)","ror":"https://ror.org/00m7gt169","country_code":"US","type":"company","lineage":["https://openalex.org/I68384125"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sally Gao","raw_affiliation_strings":["Thomson Reuters Labs, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Thomson Reuters Labs, United States","institution_ids":["https://openalex.org/I68384125"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5042650113"],"corresponding_institution_ids":["https://openalex.org/I4210121390"],"apc_list":null,"apc_paid":null,"fwci":2.7985,"has_fulltext":false,"cited_by_count":30,"citation_normalized_percentile":{"value":0.91948791,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9962000250816345,"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":0.9962000250816345,"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/T13643","display_name":"Artificial Intelligence in Law","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/3320","display_name":"Political Science and International Relations"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9940000176429749,"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":0.9788081049919128},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7524732351303101},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.7217223048210144},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6015784740447998},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5836784839630127},{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.5634159445762634},{"id":"https://openalex.org/keywords/attribution","display_name":"Attribution","score":0.5401813387870789},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.5357258915901184},{"id":"https://openalex.org/keywords/source-document","display_name":"Source document","score":0.528026282787323},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5109269022941589},{"id":"https://openalex.org/keywords/authorship-attribution","display_name":"Authorship attribution","score":0.42023730278015137},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3512597680091858},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.17974376678466797},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.10874894261360168},{"id":"https://openalex.org/keywords/social-psychology","display_name":"Social psychology","score":0.07491585612297058},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.06947559118270874}],"concepts":[{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.9788081049919128},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7524732351303101},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.7217223048210144},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6015784740447998},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5836784839630127},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.5634159445762634},{"id":"https://openalex.org/C143299363","wikidata":"https://www.wikidata.org/wiki/Q900584","display_name":"Attribution","level":2,"score":0.5401813387870789},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.5357258915901184},{"id":"https://openalex.org/C105888452","wikidata":"https://www.wikidata.org/wiki/Q7565148","display_name":"Source document","level":2,"score":0.528026282787323},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5109269022941589},{"id":"https://openalex.org/C3020202489","wikidata":"https://www.wikidata.org/wiki/Q2032038","display_name":"Authorship attribution","level":2,"score":0.42023730278015137},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3512597680091858},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.17974376678466797},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.10874894261360168},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.07491585612297058},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.06947559118270874},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C175444787","wikidata":"https://www.wikidata.org/wiki/Q39072","display_name":"Microeconomics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3411763.3443441","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3411763.3443441","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6399999856948853,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1979290264","https://openalex.org/W2282821441","https://openalex.org/W2493343568","https://openalex.org/W2612675303","https://openalex.org/W2953522645","https://openalex.org/W2964098600","https://openalex.org/W2981731882","https://openalex.org/W3099877573","https://openalex.org/W4226065182","https://openalex.org/W6752264108"],"related_works":["https://openalex.org/W4205570701","https://openalex.org/W2975078241","https://openalex.org/W2623063325","https://openalex.org/W3014920262","https://openalex.org/W4390041575","https://openalex.org/W4211252716","https://openalex.org/W61831227","https://openalex.org/W4381300082","https://openalex.org/W2785821657","https://openalex.org/W4310918653"],"abstract_inverted_index":{"This":[0,141],"study":[1,72,169],"tested":[2],"two":[3,59],"different":[4,60],"approaches":[5,28],"for":[6,131,151,185],"adding":[7],"an":[8],"explainability":[9,184],"feature":[10],"to":[11,30,50,113,157,160,182],"the":[12,32,35,43,55,81,90,108,124,132,136,144,153,162,173,176,189],"implementation":[13],"of":[14,42,62,139,167,178],"a":[15,22,99,103,129],"legal":[16],"text":[17,45,63,110],"summarization":[18,190],"solution":[19],"based":[20,85,97],"on":[21,86,98],"Deep":[23],"Learning":[24],"(DL)":[25],"model.":[26],"Both":[27],"aimed":[29],"show":[31],"reviewers":[33],"where":[34],"summary":[36,112],"originated":[37],"from":[38,89],"by":[39,54],"highlighting":[40],"portions":[41],"source":[44,100,109],"document.":[46],"The":[47,71,117,165],"participants":[48,75,118,154],"had":[49,147],"review":[51],"summaries":[52],"generated":[53],"DL":[56,91,125,186],"model":[57,126],"with":[58,66,83,95],"types":[61],"highlights":[64,68,84,96,134,146],"and":[65,111,127,175],"no":[67],"at":[69],"all.":[70],"found":[73],"that":[74,106],"were":[76,155],"significantly":[77],"faster":[78],"in":[79,123,188],"completing":[80],"task":[82],"attention":[87,133,145],"scores":[88],"model,":[92],"but":[93],"not":[94],"attribution":[101],"method,":[102],"model-agnostic":[104],"formula":[105],"compares":[107],"identify":[114],"overlapping":[115],"language.":[116],"also":[119],"reported":[120],"increased":[121],"trust":[122],"expressed":[128],"preference":[130],"over":[135],"other":[137],"type":[138],"highlights.":[140],"is":[142],"because":[143],"more":[148],"use":[149,158],"cases,":[150],"example,":[152],"able":[156],"them":[159],"enrich":[161],"machine-generated":[163],"summary.":[164],"findings":[166],"this":[168],"provide":[170,183],"insights":[171],"into":[172],"benefits":[174],"challenges":[177],"selecting":[179],"suitable":[180],"mechanisms":[181],"models":[187],"task.":[191]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-05-03T08:25:01.440150","created_date":"2025-10-10T00:00:00"}