<p>A possible way to go about this is to assign those topics to the sentences in each section [1]. As it seems that you have annotated data, you can train a "sentence topic/section model" with that. According to [1], even a multinomial naïve Bayes classifier does the job already pretty well.</p>

<p>As to the summarization aspect, unless you have training data, I would look into <em>extractive</em> summarization techniques [2] - that is, selecting the best sentences from the existing ones for the summary. The work of [2], LexRank, has a few implementations in the wild you can use. If you have summaries to learn from, you can look into <em>abstractive</em> techniques that generate new sentences from the existing ones [3], too. If you check [4], [3] has some sample implementations floating around.</p>

<p>[1] <a href="http://bioinformatics.oxfordjournals.org/content/25/23/3174.full" rel="nofollow">http://bioinformatics.oxfordjournals.org/content/25/23/3174.full</a></p>

<p>[2] <a href="http://jair.org/papers/paper1523.html" rel="nofollow">http://jair.org/papers/paper1523.html</a></p>

<p>[3] <a href="http://arxiv.org/abs/1509.00685" rel="nofollow">http://arxiv.org/abs/1509.00685</a></p>

<p>[4] <a href="http://gitxiv.com/" rel="nofollow">http://gitxiv.com/</a> </p>
Autogenerate summaries based on pre-existing topics?
<p>I'd like to do the following given a document:</p>

<ul>
<li>create a summary using pre-existing topics </li>
</ul>

<p>In the first scenario, documents are neatly organized in a uniform way. 
For example, most Wikipedia movie articles have the following subtopics (ex: <a href="https://en.wikipedia.org/wiki/Between_Us_(2012_film)" rel="nofollow">https://en.wikipedia.org/wiki/Between_Us_(2012_film)</a>)</p>

<ul>
<li>Plot</li>
<li>Cast</li>
<li>Reception</li>
<li>other optional topics</li>
</ul>

<p>In the second scenario, documents contain the same info as above; however,  documents do NOT have clean organization. Documents may use the same or similar language but organized differently. </p>

<p>In both cases, given the subtopics, I'd like to extract this info from a document.</p>

<p>Are there any machine learning/natural language processing strategies/algorithms that I can use? Combination of algorithms is fine. Algorithms that mostly work are also fine.</p>

<p>Update: It looks like what I want is <em>Information Extraction</em>.</p>