We were discussing this on MetaOptimize recently (http://metaoptimize.com/qa/questions/2815/how-are-search-eng...), but I'm curious to hear about alternate approaches.
It's 5 years old now, but the summaries it generates are competitive quality-wise with most things out there (eg, the MS Word summarizer). Unfortunately I don't have an online demo working atm (like I said - 5 years old)
My algorithm is something I made up, and from memory it works like this:
1) Remove HTML, stem, remove stopwords etc
2) Sort unique words by popularity in the text
3) Split the original text on sentence boundaries.
4) Include each sentence that first mentions the next most popular word, until the summary is the maximum length requested.
Like most things, it's surprising how well a simple algorithm like that works.
There are ports for C#, and Googling just then apparently someone has done a python port too.
http://www.sfgate.com/cgi-bin/article.cgi?f=/c/a/2010/10/18/...
suffered from complete content extraction failure.
The third one seemed to work pretty well, but there were lots of spurious newlines in the output which made it really hard to read.
Nice idea but needs another 48 hours of polish :-)
Maybe you could pipe requests through their API: