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Comment by midko | Hacker News Reader

Hi. I study CS with an inclination towards ML but I don't know anything about the topic of automated summarizing. I'm curious, since you've taken a ML approach, did you still need to rely on NLP and if so, was this very problematic? Also, do you perhaps know an article or a paper that could serve as a good starting point/overview of what approaches there are to summarizing and what are the current difficulties. Thanks

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Hi, I recommend this following research: http://www.cs.cmu.edu/~nasmith/LS2/das-martins.07.pdf http://www.aclweb.org/anthology-new/W/W03/W03-1204.pdf

I still have other papers, but those can be a good starting point.

In my thesis, NLP is done via statistical approach. It learns from it's previous summaries does have somewhat learning. I don't see any problems combining NLP with Machine Learning. Can you elaborate on this?

Thanks, these look like exactly what I was looking for.

Re NLP, I meant to say NLP from sentiment analysis perspective (not sure if that's the right way to put it). So my question was whether you had to extract meaning of one or few related sentences or only process the text in a statistical manner (which you answered).

Look up text rank. Essentially it amounts to representing text as a graph with sentences as nodes and some sort of similarity measure as edge weights. You then run page rank on that graph.

Interesting. Thanks! :)

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