<p>This paper compared a few different strategies: <a href="https://arxiv.org/abs/1905.05583" rel="noreferrer">How to Fine-Tune BERT for Text Classification?</a>. 
On the IMDb movie review dataset, they actually found that cutting out the middle of the text (rather than truncating the beginning or the end) worked best! It even outperformed more complex "hierarchical" approaches involving breaking the article into chunks and then recombining the results.</p>

<p>As another anecdote, I applied BERT to the Wikipedia Personal Attacks dataset <a href="https://youtu.be/_eSGWNqKeeY" rel="noreferrer">here</a>, and found that simple truncation worked well enough that I wasn't motivated to try other approaches :) </p>
How to use Bert for long text classification?
<p>We know that BERT has a max length limit of tokens = 512, So if an article has a length of much bigger than 512, such as 10000 tokens in text
How can BERT be used?</p>