I am currently working on the automation of recurring reports (weekly 30-50 pages reports for around 100 districts). Those reports have a mostly fixed form : maps, graphs, data tables and small zone of text.
Apart for some discussion around colors and legends, it isn't difficult to automate the production of maps / graphs / tables. (I work with Rmarkdown if you want to know)
However, for the text, a simple approach like writing 'r value' in markdown to produce a variable value inside of the text feel 'too automated'. The reports end up having ten sentences like 'During the last quarter (QX 201X) total result was XXX (a +X% growth compared to the same quarter the previous year).'
I'd like to get automatic variations of that phrase without modyfiying it's meaning. I've ended up writing half a dozen variations myself. But (1) it still feels repetitive and unnatural, and (2) doing it for every phrase of the report may take a lot of time.
We have seen a lot of extraordinary things in transfering things for visual representation (see : https://en.wikipedia.org/wiki/Neural_Style_Transfer). So I was wondering if we have similar things for NLP, that would allow a text to be rewritten using a different 'style' (a neutral style -or an absence of style- in my case), keeping it's main content. The main paper I found on the subject is titled 'What is wrong with style transfer for texts?' and shows why style transfer doesn't really work for texts. Given (1) the constraint (keeping the same meaning) and (2) it's formalism (I know which number should be shown), I feel like the problem may be simpler than the whole style transfert.
Any idea where to start to automatically write variations of a text while keeping it's meaning constant ?