Word embedding of a new word which was not in training
Generating word embeddings for "OOV"(out of vocabulary) words is one of the major limitations of many standard embeddings like Glove and word2vec. However, fastText circumvents this problem to some extent.  

Instead of the traditional approaches which have distinct vectors for each word, they take a character n-grams level representation.
For  instance,  a  word  with n=  3,  will  be  represented  by  the  character  n-grams:

&lt;wh, whe, her, ere, re&gt;

and  the  special  sequence:

&lt; where &gt;

Here,&lt;>are part of the n-grams.  

$$
s(w,c) = \sum_{g\varepsilon G_{_{w}}} z_{g}^{T} v_{c}
$$
Here, $G$ represents the size of a dictionary of n-grams, and given a word $w$, then $G_{w}\subset \left \{ 1, ..., G \right \}$ represents the set of n-grams appearing in
$w$. They associate a vector representation $z_{g}$ to each
n-gram $g$ and represent a word by the sum of the
vector representations of its n-grams.

This helps them tackle OOV words via knowing some representations of a subword.
For an instance, an OOV word: 
sechero 

The 3-gram:

&lt;se, sec, ech, che, her, ero, ro&gt;

since, these 3-grams may have been encountered during learning, through other known words, like:

&lt;se - section
che - cheer
ro&gt; - hero

Hence, it can form at least some sensible embedding, instead of returning a useless &lt;UNK&gt;

Fastext in fact is an extension to word2vec, with majorly the feature explained above.