<p>The keyword is <a href="https://en.wikipedia.org/wiki/Automatic_summarization" rel="nofollow"><strong>Automatic Summarization</strong></a>.</p>

<p>Generally, there are two approaches to automatic summarization: <strong><em>extraction</em></strong> and <strong><em>abstraction</em></strong>.</p>

<ul>
<li>Extractive methods work by selecting a subset of existing words, phrases, or sentences in the original text to form the summary.</li>
<li>Abstractive methods build an internal semantic representation and then use natural language generation techniques to create a summary that is closer to what a human might generate.</li>
</ul>

<p>Abstractive summarization is a lot more difficult. An interesting, approach is described in <a href="http://arxiv.org/abs/1509.00685" rel="nofollow">A Neural Attention Model for Abstractive Sentence Summarization</a> by Alexander M. Rush, Sumit Chopra, Jason Weston (source code based on the paper <a href="https://github.com/facebook/NAMAS" rel="nofollow">here</a>).</p>

<p>A "simple" approach is used in Word (<a href="https://support.office.com/en-us/article/Automatically-summarize-a-document-b43f20ae-ec4b-41cc-b40a-753eed6d7424" rel="nofollow">AutoSummary Tool</a>): </p>

<blockquote>
  <p>AutoSummarize determines key points by analyzing the document and assigning a score to each sentence. Sentences that contain words used frequently in the document are given a higher score. You then choose a percentage of the highest-scoring sentences to display in the summary.</p>
  
  <p>You can select whether to highlight key points in a document, insert an executive summary or abstract at the top of a document, create a new document and put the summary there, or hide everything but the summary.</p>
  
  <p>If you choose to highlight key points or hide everything but the summary, you can switch between displaying only the key points in a document (the rest of the document is hidden) and highlighting them in the document. As you read, you can also change the level of detail at any time.</p>
</blockquote>

<p>Anyway automatic data (text) summarization is an active area of machine learning / data mining with many ongoing researches. You should start reading some good overviews:</p>

<ul>
<li><a href="http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings2/sum-mani.pdf" rel="nofollow">Summarization evaluation: an overview</a> by Inderjeet Mani.</li>
<li><a href="https://www.cs.cmu.edu/~afm/Home_files/Das_Martins_survey_summarization.pdf" rel="nofollow">A Survey on Automatic Text Summarization</a> by Dipanjan Das André F.T. Martins (emphasizes extractive approaches to summarization using statistical methods).</li>
</ul>
Summarization Algo for novels : Supervised learning
<p>I want to write a Learning Algo which can automatically create summary of articles .</p>

<p>e.g, there are some fiction novels(one category considering it as a filter) in PDF format. I want to make an automated process of creating its summary. 
We can provide some sample data to implement it in supervised learning approach. 
Kindly suggest me how can i implement this properly.</p>

<p>I am a beginner &amp; am pursuing Andrew Ng course and aware of some common algorithms(linear reg, logistic , neural net) + Udacity Statistics courses and ready to dive more into NLP , Deep learning etc. but motive is to solve this. :)
Thanks in advance </p>