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Methods that I know of

  • Bag of words + weighting: tf-idf, bm25
  • Topic models: LSA, LDA
  • Word/sentence/document embedding

Are there other commonly used methods to represent a document by a vector?

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    $\begingroup$ LSTM Autoencoders. $\endgroup$ – DeltaIV Jun 29 '18 at 6:40
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Aside from unsupervised methods like doc2vec, there are couple of supervised methods:

All of them aims to create vector representations for documents, so dot product of vectors would represent semantically similar of documents.

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In the topic models category there is also NMF (Non-negative matrix factorization)

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