What to do on immediate rejection from journal citing "Similarity Score" that is too high, but specific instances are just boilerplate text? I have received an immediate reject notice from the journal ACM CSUR mentioning that my written survey had 13% similar materials with respect to the online available sources. Passing through the instances one by one, it sounds to me that the majority are just the text of the references to the citations, e.g., "Authors et al. [first_author et al. Year]", some standard terms such as "performance", "tuning", and many standard ACM sections such as the ACM Copyright section, etc. 

P.S: 

1- To be noted that I am aware of plagiarism policy and I strongly support it. However, the fact that we are dealing with a survey journal instead of a regular computer science experimental research paper made me confused on the outcome of the similarity check and the immediate rejection.

2- The application that does this check is called "iThenticate".

Had anyone experienced the same issue ? Normally what do we do to proceed in this status? Does ACM have a fixed threshold on this similarity score?
I cannot find anything about the use of the ithenticate in the ACM CSUR instructions for authors. The ACM magazine Inroads says:


  Please note that ACM Inroads as well as all ACM publications expect new, unpublished material, unless specifically reprinting an item from another publication with attribution. In support of this goal, ACM Inroads uses iThenticate to develop a similarity index for all submissions. As a first step after an initial submission, iThenticate compares the text to published material (at least 43 billion web pages and over 130 million content items). It provides editors with a set of similarity indices and helps them identify original material, including prior publications of authors. 


This suggests that there is not a hard cut off, but rather a set of metrics and comparisons that the editors use in making a decision. While it is possible that the ACM system is poorly tuned, this seems unlikely. In my experience, a 13% similarity is quite high and is often indicative of issues.

While it is not cheap, you can purchase a single paper use of iThenticate. It appears that iThenticate is owned by TurnItIn which many universities have access to. It is not clear what the different systems do, but it may help you identify what is being flagged up.