Help to choose algorithm for computing difference between 2 texts? I have a task to create a tool, which will be able to find articles-duplicates of a given reference article.
I know word vectorization (tf-idf,word2vec), RNN methods, but i can not choose something suitable for my situation.

My requirments:


data are being collected on the fly  (program parses articles from web sites, so i don't have regular DB with collection of texts)
there is a reference text, whose copies need to be found
copies could be copypasted, partially copypasted (by paragraphs) or paraphrased
reference-vs-copy comparison algorithm is preferable, but not required (instead of reference-vs-corps)
algorithm shouldn't do deep semantic analyzis, only kind of word counting, word vectorization, substring search
instead one algorithm, i can use a set of herurisitcs
algorithms can do false positive dicisions


I come up with such ideas:


download pretrained word2vec and compare means of word-vectors
Build a dictionary word->count from every text and compare it to reference dictionary
collect about 100 texts, vectorize them according to tf-idf and find closest to the reference


I will apreciate, if you will point specific algorithms, libs, examples based on key-word extractions, dummy substring search, line difference comparison for python or CLI.
algorithm shouldn't do deep semantic analyzis, only kind of word counting, word vectorization, substring search


For such tasks, test if built-in Elastic Search / SOLR models work well. These products have multiple models that can be tuned with configuration. With these models, you provide a document and find other documents that are "similar".

Some of the available algorithms are : 


Cosine Similarity 
Okapi_BM25
DFR
LM Jelinek Mercer 


Docs : 

https://www.elastic.co/guide/en/elasticsearch/reference/current/index-modules-similarity.html

https://lucene.apache.org/solr/guide/6_6/morelikethis.html