I have a dataset of sentences in a non english language like :

word1 word2 word3 word62
word5 word1 word2

and the length of each sentence is not fixed, now i want to represent each sentence as a fixed sized vector and give it to my model and i want to keep as much information as possible in the embedding, and i dont want to have a maximum length for sentences because important information might happen in the end.
the only two approaches i can think of so far is :

convert them to one hot vector and add them
convert them to a word embedding and then add them

my questions are :

what will happen if i use word2vec and embed them, then add the words in the sentence this way to represent the sentence, is this better than using hot one vectoring and adding them instead?
is there any better way? what is the best approach to represent a variable length sentence without losing information from it like having a maximum length for each sentence ? ( i want all the words in the sentence to affect the embedding )