<p>This is an open problem; many approaches exist to creating meaningful sentence vectors.</p>

<ul>
<li>BoW models, as Fabrizio_P explained</li>
<li>Element-wise vector operations (<a href="http://www.aclweb.org/anthology/P/P08/P08-1028.pdf" rel="nofollow noreferrer">http://www.aclweb.org/anthology/P/P08/P08-1028.pdf</a>)

<ul>
<li>Addition (i.e. simply add all the word vector together, possibly normalizing afterwards)</li>
<li>Multiplication (i.e. multiply all vectors together, element-wise, resulting in a logically grounded embedding)</li>
</ul></li>
<li>Arbitrary task-specific recurrent functions (<a href="http://www.aclweb.org/anthology/D12-1110" rel="nofollow noreferrer">http://www.aclweb.org/anthology/D12-1110</a>)</li>
<li>More sophisticated general-purpose approaches (<a href="https://arxiv.org/abs/1508.02354" rel="nofollow noreferrer">https://arxiv.org/abs/1508.02354</a>, <a href="https://arxiv.org/abs/1506.06726" rel="nofollow noreferrer">https://arxiv.org/abs/1506.06726</a>)</li>
</ul>

<p>Element-wise operations, such as vector addition, suffice for most simple tasks, but obviously exhibit a high amount of information loss as sentences grow larger or the task at hand gets more demanding. Recurrent neural networks are quite good at creating task specific sentence embeddings, but obviously these require training data and some familiarity with machine learning. General purpose sentence embeddings are the most interesting ones from a research perspective, but probably not what you're looking for.</p>
How to embed a sentence into vector
<p>I had the sentence.I use word2vec to embed word to vector.For example, consider I have a sentence of 5 words.so I get 5 different vectors(One for each word) for the sentence.Which is the best method to make the complete sentence as a single vector which I will pass to the ANN?</p>