Sorry, this isn't rocket science at all. Standard clustering algorithms (found in any off-the-shelf natural text processing library) and text summation with libots should suffice for most of the heavy lifting. http://tldr.it/ http://libots.sourceforge.net/ Further, most news articles' first paragraph is a practical (although you may have not noticed) summary. Coming from NLP, unless you can influence the source and t…
I experienced an article in TheVerge which is mainly a video as its content. What I will be amazed is a good automatic summarization algorithm that is using abstraction and not just extraction. Also, check out circa ( http://cir.ca/ ). Never tried, but as I read, it uses both human and algorithm to "summarize" articles.
Further, many of the news really originate from relatively limited sources (reuters, etc), so you can plug your solution there as well.
Therefore it should be OK to assume that if you put humans at the same pipeline to summarize news manually, the capacity and efficiency will be reasonable.