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Diffbot's stuff is in a different league (but it's a hosted service with a large dataset) but if anyone's vaguely interested in this area, I've been working on a Ruby library that performs some similar features: https://github.com/peterc/pismo

It is currently undergoing the "big rewrite" (which includes some proper classification work rather than shooting in the dark), however, but it's still in daily use on several sites. Hopefully I can learn a few lessons from Diffbot!

I should also point out BoilerPlate - http://code.google.com/p/boilerpipe/ - an interesting Java based content extraction project that's being worked on an by an actual PhD student rather than a dilettante like me ;-) Again, Diffbot's stuff goes a lot further than this but there are lessons to be learned nonetheless.

Last but not least, a paper by the aforementioned PhD student called Boilerplate Detection using Shallow Text Features is available at http://www.l3s.de/~kohlschuetter/boilerplate/

I suspect that there's going to be a lot more work in these areas in the medium term because of the growth of the "e-discovery" market and because the dreams of a consistently marked up "semantic Web" have been washing down the pan for a while now.



That is good stuff. How is http://coder.io using these?


Are some examples or online trial available for pismo?


There is an online demo of boilerpipe: http://boilerpipe-web.appspot.com/

I use boilerpipe a lot and highly recommend it. We've discussed it before on MetaOptimize: http://metaoptimize.com/qa/questions/3440/text-extraction-fr...

I ran a few qualitative tests on Diffbot's "Article API" and the results also look good. I haven't gotten a chance to run a detailed or quantitative comparison.


Nothing specific because it's really just a library, though I'm now considering this for the next version :-) However, http://coder.io/ leans heavily on it and all of the summaries and titles there come from it.




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