<p>I will post my own answer so that we could have some sort of voting here. I am obviously for detailed excerpts.</p>

<p>After my post, @whuber edited all excerpts that I mentioned to shorten them drastically. Here are the edits so that people can compare which version they prefer:</p>

<ul>
<li><a href="https://stats.stackexchange.com/questions/tagged/elastic-net" class="post-tag" title="show questions tagged &#39;elastic-net&#39;" rel="tag">elastic-net</a>: <a href="https://stats.stackexchange.com/posts/184175/revisions">https://stats.stackexchange.com/posts/184175/revisions</a></li>
<li><a href="https://stats.stackexchange.com/questions/tagged/total-least-squares" class="post-tag" title="show questions tagged &#39;total-least-squares&#39;" rel="tag">total-least-squares</a>: <a href="https://stats.stackexchange.com/posts/184871/revisions">https://stats.stackexchange.com/posts/184871/revisions</a></li>
<li><a href="https://stats.stackexchange.com/questions/tagged/distance-covariance" class="post-tag" title="show questions tagged &#39;distance-covariance&#39;" rel="tag">distance-covariance</a>: <a href="https://stats.stackexchange.com/posts/177139/revisions">https://stats.stackexchange.com/posts/177139/revisions</a></li>
</ul>

<p>I find that shortening e.g. the first excerpt from </p>

<blockquote>
  <p>A regularization method for regression models that penalizes the size of regression coefficients $\beta_i$ and biases them towards zero. Elastic net includes two penalty terms, one proportional to $\sum |\beta_i|$ and another proportional to $\sum \beta_i^2$. When used alone, these penalty terms lead to Lasso regression and ridge regression respectively.</p>
</blockquote>

<p>to</p>

<blockquote>
  <p>A regularization method for regression models.</p>
</blockquote>

<p>makes it useless. Lasso, ridge, and elastic net could have exactly the same excerpt then. Honestly, I am baffled.</p>

<hr>

<p><strong>Update (May 19, 2017)</strong></p>

<p>Other answers (+1 to each) convinced me that conciseness <em>is</em> important and made me change my opinion. After all the discussions, we arrived to something like a compromise (but more on the @whuber's side of the spectrum). For <code>[elastic-net]</code>, the excerpt reads as follows:</p>

<blockquote>
  <p>A regularization method for regression models that combines the penalties of lasso and of ridge regression. </p>
</blockquote>

<p>and by now I agree that this is better than my version above.</p>
