<p>1) Skip gram method: <a href="https://www.google.co.in/url?sa=t&amp;rct=j&amp;q=&amp;esrc=s&amp;source=web&amp;cd=1&amp;cad=rja&amp;uact=8&amp;ved=0CCMQFjAA&amp;url=http%3A%2F%2Fpapers.nips.cc%2Fpaper%2F5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf&amp;ei=FdyHVeeNDMqhugSShZugAg&amp;usg=AFQjCNH4-Ecded1JdFimktgajA3mvcdXaQ&amp;sig2=5hiJmQ0XYxBX639t-gJ2jw&amp;bvm=bv.96339352,d.c2E">paper here</a> and the tool that uses it, <a href="https://code.google.com/p/word2vec/">google word2vec</a></p>

<p>2) Using <a href="http://arxiv.org/abs/1502.06922">LSTM-RNN</a> to form semantic representations of sentences.</p>

<p>3) Representations of <a href="https://www.google.co.in/url?sa=t&amp;rct=j&amp;q=&amp;esrc=s&amp;source=web&amp;cd=1&amp;cad=rja&amp;uact=8&amp;ved=0CCIQFjAA&amp;url=http%3A%2F%2Fcs.stanford.edu%2F~quocle%2Fparagraph_vector.pdf&amp;ei=Ct2HVa-9GcOMuAS3-oDoDA&amp;usg=AFQjCNESECVF_9eXAkAjfSqqHrqlxkVQgg&amp;sig2=ozIjjEKK9rqrn4T0wabTQw&amp;bvm=bv.96339352,d.c2E">sentences and documents</a>. The Paragraph vector is introduced in this paper. It is basically an unsupervised algorithm that learns fixed-length feature representations from variable-length pieces of texts, such as sentences, paragraphs, and documents.</p>

<p>4) Though this <a href="http://arxiv.org/abs/1503.00075">paper</a> does not form sentence/paragraph vectors, it is simple enough to do that. One can just plug in the individual word vectors(<a href="http://nlp.stanford.edu/pubs/glove.pdf">Glove</a> <a href="http://nlp.stanford.edu/projects/glove/">word vectors</a> are found to give the best performance) and then can form a vector representation of the whole sentence/paragraph. </p>

<p>5) Using a <a href="http://arxiv.org/pdf/1406.3830.pdf">CNN</a> to <a href="http://arxiv.org/abs/1502.01710">summarize</a> documents.</p>
How can a sentence or a document be converted to a vector?
<p>We have models for converting words to vectors (for example the word2vec model). Do similar models exist which convert sentences/documents into vectors, using perhaps the vectors learnt for the individual words?</p>