At a cursory glance the algorithm seems like a variation Luhn's abstract algorithm.
What is the most important sentence in this:
Drakaal is a poopy head. He often posts to hackernews and calls people an idiot. When this happens I get mad.
The Premise is "Drakaal is a poopy head" so that is the most important, but it doesn't inform the user. What he does is the most telling of the sentences, but with the "He" as the first word you can't actually make sense of the content with out the prior sentence. The last sentence is the least important for the understanding of the content.
It is important to know that sentence number 2 is the most informative, but that to figure out what it means requires Sentence 1.
When computing the results of a summary you have to weigh sentence dependencies, density of information, amount of emotion expressed and number of characters available to you.
And Keywords aren't enough, you need noun entities and the ability to tell the relationships of words so that you know "Cars, Trucks, and Automobiles" are all the same concept in many contexts.
Summarizing only blogs posts seems a bit limiting to me. (Btw, I'm not trying to be negative, congrats on your success! texteaser looks great!)
I implemented a custom version (mainly changed the scoring scheme to include TF/IDF of words for initialized scoring) of TextRank and loved it.
The main thing I liked about it was how general it was. Words are nodes and sentences are vertices. Then you basically use pagerank to rank the sentences according the graph representation.
I'm a little bit familiar with TextRank because I stumbled upon it when I'm doing my research. I also read several algorithms but forgot what they are called.
News is the most broadly applicable use for this so leveraging that isn't a bad thing. There's always a trade off of broad applicability vs overfitting for a particular case to get better results.
Thanks for the insight! Again great work.