<p>I think you can adapt the architecture for <a href="https://arxiv.org/pdf/1411.4555.pdf" rel="nofollow">image captioning</a> which uses a CNN for input analysis feeding a RNN for language generation.</p>

<p>For your title generation application, think of it as document captioning instead of image captioning and train a <a href="http://www.nlpr.ia.ac.cn/cip/~liukang/liukangPageFile/Recurrent%20Convolutional%20Neural%20Networks%20for%20Text%20Classification.pdf" rel="nofollow">Recurrent CNN</a> on the document text and the RNN on the title text.</p>

<p>This is pretty ambitious for a beginner, so I suggest you start "simplier" with off-the-shelf-examples of <a href="http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/" rel="nofollow">CNN</a> and <a href="https://www.tensorflow.org/versions/r0.8/tutorials/recurrent/index.html" rel="nofollow">RNN</a> to understand the components and basic concepts of deep learning.</p>
How to predict the title of news articles by using deep learning?
<p>I'm interested in studying deep learning.
Recently, I've researched text mining using news articles dataset.</p>

<p>I want to extract the important one or two sentences from a body of article.
So, I mimic this problem as approximated version, that is, to find a title of article. </p>

<p>The training examples will be like this:
x is a collection of body of the articles, and 
y is a collection of title of the articles.</p>

<p>But, test examples only have x. (without y)
After training a model, I predict the title of test set, only using its body. </p>

<p>How can I build the model?
As beginner of deep learning, I want to have some insight or hint for this problem.</p>

<p>Thanks!</p>