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So how does this compare to the stuff Google was doing with document summarization? Is the content unique, meaning does it summarize using brand new words? It'

So how does this compare to the stuff Google was doing with document summarization? Is the content unique, meaning does it summarize using brand new words? It's unclear but still seems Really promising.

thpalmear 10y ago
Google has been to busy trying to lift methods from Berkeley Lab and passing it off as their own, in particular Tomas Mikolov https://www.kaggle.com/c/word2vec-nlp-tutorial/forums/t/12349/word2vec-is-based-on-an-approach-from-lawrence-berkeley-national-lab https://www.kaggle.com/c/word2vec-nlp-tutorial/forums/t/1234...
- wodenokoto 10y ago
  You need a better argument than "somebody else has been working on word vectors before Google" No shit.
  - thpalmear 10y ago
    That's not what was said. It's how the feature attributes in the vectors are constructed, scored and ranked in addition to the calculations used to score vectors for similarity.
deepGem 10y ago
Just going by the GitHub readme and the corresponding paper this tool does not generate new words, popularly known as the abstraction summary. This is doing an extraction task followed by a syntactic compression task. Pages 3,4 of the paper http://www.cs.utexas.edu/~gdurrett/papers/durrett-berg-klein-acl2016.pdf http://www.cs.utexas.edu/~gdurrett/papers/durrett-berg-klein...