NLP : variations of a text without modifying it's meaning
Text summarization can be divided into two categories 1. Extractive Summarization and  2. Abstractive Summarization

Extractive Summarization: These methods rely on extracting several parts, such as phrases and sentences, from a piece of text and stack them together to create a summary. Therefore, identifying the right sentences for summarization is of utmost importance in an extractive method.
Abstractive Summarization: Abstractive methods select words based on semantic understanding, even those words did not appear in the source documents. It aims at producing important material in a new way. They interpret and examine the text using advanced natural language techniques to generate a new shorter text that conveys the most critical information from the original text.

What you are looking for is abstractive summarisation. Since you are working in R there is a nice library called lexRank taking an example from here would look something like
#load needed packages
library(xml2)
library(rvest)
library(lexRankr)

#url to scrape
monsanto_url = &quot;https://www.theguardian.com/environment/2017/sep/28/monsanto-banned-from-european-parliament&quot;
   
#read page html
page = xml2::read_html(monsanto_url)
#extract text from page html using selector
page_text = rvest::html_text(rvest::html_nodes(page, &quot;.js-article__body p&quot;))

#perform lexrank for top 3 sentences
top_3 = lexRankr::lexRank(page_text,
                          #only 1 article; repeat same docid for all of input vector
                          docId = rep(1, length(page_text)),
                          #return 3 sentences to mimick /u/autotldr's output
                          n = 3,
                          continuous = TRUE)

#reorder the top 3 sentences to be in order of appearance in article
order_of_appearance = order(as.integer(gsub(&quot;_&quot;,&quot;&quot;,top_3$sentenceId)))
#extract sentences in order of appearance
ordered_top_3 = top_3[order_of_appearance, &quot;sentence&quot;]

&gt; ordered_top_3
[1] &quot;Monsanto lobbyists have been banned from entering the European parliament after the multinational refused to attend a parliamentary hearing into allegations of regulatory interference.&quot;
[2] &quot;Monsanto officials will now be unable to meet MEPs, attend committee meetings or use digital resources on parliament premises in Brussels or Strasbourg.&quot;                                
[3] &quot;A Monsanto letter to MEPs seen by the Guardian said that the European parliament was not “an appropriate forum” for discussion on the issues involved.&quot;  

EDIT: How I like to think about abstractive summarisation: Y
Using encoder-decoder architecture (extendended with transformers) for seq2seq problems you can essentially get an embeding of your text, where same sentences can be embedded differently in different context, giving same/similiar output.