Multiple results from one variable when using the tm "stemCompletion" method

I have a corpus containing log data from 15 observations of three variables (ID, title, abstract). Using R Studio, I am reading data from a CSV file (one line for each observation). While doing some text mining operations, I am having trouble using the stemCompletion method. After applying stemCompletion, I observed that the results are shown for each .csv line three times. All other tm methods (for example stemDocument) produce only one result. I am wondering why this is happening and how I can solve the problem.

I used the following code:

data.corpus <- Corpus(DataframeSource(data))  
data.corpuscopy <- data.corpus
data.corpus <- tm_map(data.corpus, stemDocument)
data.corpus <- tm_map(data.corpus, stemCompletion, dictionary=data.corpuscopy) 

      

        
        
        
      

    

The only results after applying stemDocument are, for example,

"> data.corpus[[1]]

physic environ   sourc  innov investig  attribut  innov space
          investig  physic space intersect  innov  innov     relev attribut  physic space   innov        reflect  chang natur  innov  technolog advanc  servic  mean chang  argu   develop  innov space similar embodi  divers set  valu   collabor open  sustain use  literatur review interview  benchmark    examin  relationship  physic environ  innov         literatur review   interview underlin innov   communic  human centr process   result five attribut  innov space  present collabor enabl modifi smart attract   reflect       provid perspect   challeng    support innov creation  develop physic space   add   conceptu develop  innov space  outlin physic space   innov servic"

      

        
        
        
      

    

And after using stemCompletion, the results appear three times:

"$`1`
physical environment source innovation investigation attributes innovation space investigation physical space intersect innovation innovation relevant attributes physical space innovation reflect changes nature innovation technological advancements service meanwhile changes argues develop innovation space similarity embodies diversified set valuable collaboration open sustainability used literature review interviews benchmarking examine relationships physical environment innovation literature review interviews underline innovation communicative human centred processes result five attributes innovation space present collaboration enablers modifiability smartness attractiveness reflect provide perspectives challenge support innovation creation develop physical space addition conceptual develop innovation space outlines physical space innovation service
physical environment source innovation investigation attributes innovation space investigation physical space intersect innovation innovation relevant attributes physical space innovation reflect changes nature innovation technological advancements service meanwhile changes argues develop innovation space similarity embodies diversified set valuable collaboration open sustainability used literature review interviews benchmarking examine relationships physical environment innovation literature review interviews underline innovation communicative human centred processes result five attributes innovation space present collaboration enablers modifiability smartness attractiveness reflect provide perspectives challenge support innovation creation develop physical space addition conceptual develop innovation space outlines physical space innovation service
physical environment source innovation investigation attributes innovation space investigation physical space intersect innovation innovation relevant attributes physical space innovation reflect changes nature innovation technological advancements service meanwhile changes argues develop innovation space similarity embodies diversified set valuable collaboration open sustainability used literature review interviews benchmarking examine relationships physical environment innovation literature review interviews underline innovation communicative human centred processes result five attributes innovation space present collaboration enablers modifiability smartness attractiveness reflect provide perspectives challenge support innovation creation develop physical space addition conceptual develop innovation space outlines physical space innovation service"

      

        
        
        
      

    

Below is a sample as a reproducible example:

Csv file containing three observations of three variables:

ID;Text A;Text B
1;Below is the first title;Innovation and Knowledge Management
2;And now the second Title;Organizational Performance and Learning are very important
3;The third title;Knowledge plays an important rule in organizations

      

        
        
        
      

    

And below is the method i used

data = read.csv2("Test.csv")
data[,2]=as.character(data[,2])
data[,3]=as.character(data[,3])

corpus <- Corpus(DataframeSource(data)) 
corpuscopy <- corpus
corpus <- tm_map(corpus, stemDocument)
corpus[[1]]

corpus <- tm_map(corpus, stemCompletion, dictionary=corpuscopy)
inspect(corpus[1:3])

      

        
        
        
      

    

It seems to me that it depends on the number of variables used in the .csv, but I have no idea why.