titles = [
 "End of Year Review 2020",
 "2020 End of Year",
 "January Sales Projections",
 "Accounts 2017-2018",
 "Jan Sales Predictions"
]

# Desired output
filtered_titles = [
 "End of Year Review 2020",
 "January Sales Projections",
 "Accounts 2017-2018",
]

import spacy
from itertools import combinations


# Set globals
nlp = spacy.load("en_core_web_md")

def pre_process(titles):
 """
 Pre-processes titles by removing stopwords and lemmatizing text.
 :param titles: list of strings, contains target titles,.
 :return: preprocessed_title_docs, list containing pre-processed titles.
 """

 # Preprocess all the titles
 title_docs = [nlp(x) for x in titles]
 preprocessed_title_docs = []
 lemmatized_tokens = []
 for title_doc in title_docs:
  for token in title_doc:
   if not token.is_stop:
    lemmatized_tokens.append(token.lemma_)
  preprocessed_title_docs.append(" ".join(lemmatized_tokens))
  del lemmatized_tokens[
   :
   ] # empty the lemmatized tokens list as the code moves onto a new title

 return preprocessed_title_docs

def similarity_filter(titles):
 """
 Recursively check if titles pass a similarity filter.
 :param titles: list of strings, contains titles.
 If the function finds titles that fail the similarity test, the above param will be the function output.
 :return: this method upon itself unless there are no similar titles; in that case the feed that was passed
 in is returned.
 """

 # Preprocess titles
 preprocessed_title_docs = pre_process(titles)

 # Remove similar titles
 all_summary_pairs = list(combinations(preprocessed_title_docs, 2))
 similar_titles = []
 for pair in all_summary_pairs:
  title1 = nlp(pair[0])
  title2 = nlp(pair[1])
  similarity = title1.similarity(title2)
  if similarity > 0.8:
   similar_titles.append(pair)

 titles_to_remove = []
 for a_title in similar_titles:
  # Get the index of the first title in the pair
  index_for_removal = preprocessed_title_docs.index(a_title[0])
  titles_to_remove.append(index_for_removal)

 # Get indices of similar titles and remove them
 similar_title_counts = set(titles_to_remove)
 similar_titles = [
  x[1] for x in enumerate(titles) if x[0] in similar_title_counts
 ]

 # Exit the recursion if there are no longer any similar titles
 if len(similar_title_counts) == 0:
  return titles

 # Continue the recursion if there are still titles to remove
 else:
  # Remove similar titles from the next input
  for title in similar_titles:
   idx = titles.index(title)
   titles.pop(idx)
   
  return similarity_filter(titles)

if __name__ == "__main__":
 your_title_list = ['title1', 'title2']
 similarty_filter(your_title_list)