What distance should I use for edges weights in textrank algorithm I found this python implementation on github with 400+ stars which use levenshtein distance between each nodes.

But original paper (page 4) said: 


  Next, all lexical units that pass the syntactic filter
  are added to the graph, and an edge is added between
  those lexical units that co-occur within a window of N word


So the question is: is levenshtein distance legit for this algorithm or better to rewrite with windowed edges?

Intuitively levenshtein distance must not work because it's not denote importance of word...
As lib author said using lev-dist was just en experiment and might not work (and it really doesn't), but for some reason people use broken lib w/o questions ¯\_(ツ)_/¯