I came across two interesting papers which describe promising approaches for document classificationusing word embedding. [1] the first one is the fasttext algorithmdescribed in the paper : Bag of Tricks for Efficient Text Classificationhttps://arxiv.org/abs/1607.01759v3there is also an explanation here:https://web.stanford.edu/class/cs224n/lectures/cs224n-2017-lecture5-highlight.pdf[2] the second one called DANs described in the paper Deep Unordered Composition Rivals Syntactic Methods for Text Classification https://pdfs.semanticscholar.org/5bd3/71142c9afee9ec926d9f42b807f36294f82b.pdfwhat is the difference between both approaches ? Are they essentially the same as they both seem to average word embedding and pass it through a MLP or am I missing something crucial ? 
document classification : what is the difference betwen fasttext and DANs ?
machine learning;neural network;word2vec;similar documents