title: Extract most important keywords from a set of documents
body: <p>I have a set of 3000 text documents and I want to extract top 300 keywords (could be single word or multiple words).</p>
  
  <p>I have tried the below approaches - </p>
  
  <p><a href="https://www.airpair.com/nlp/keyword-extraction-tutorial" rel="nofollow noreferrer">RAKE</a>: It is a Python based keyword extraction library and it failed miserably.</p>
  
  <p><a href="https://en.wikipedia.org/wiki/Tf%E2%80%93idf" rel="nofollow noreferrer">Tf-Idf</a>: It has given me good keywords per document, but it is not able to aggregate them and find keywords that represent the whole group of documents.
  Also, just selecting top k words from each document based on Tf-Idf score won't help, right?</p>
  
  <p><a href="https://deeplearning4j.org/word2vec" rel="nofollow noreferrer">Word2vec</a>: I was able to do some cool stuff like find similar words but not sure how to find important keywords using it.</p>
  
  <p>Can you please suggest some good approach (or elaborate on how to improve any of the above 3) to solve this problem? Thanks :)</p>