Hi redditors,

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I am currently working on a project which includes a topic extraction pipeline and now I want to create document embeddings using the Google BERT model instead of good old Tf-Idf. Sadly BERT has a limit on the input size and therefore I cannot push whole texts into it. Now I need to encode each sentence and generate a document feature vector out of it. I had a look at the literature, but that does not seem to be an active research problem. Are you aware of any techniques which encode e.g. the semantic structure of the sequence into such a vector?