[gibrown]

Interesting. This basically uses the background word2vec data for the entire Web to provide more information and help with things like disambiguation, synonyms, etc? Am I understanding that correctly?

Maybe nit-picky thought, but its not clear to me that the TF-IDF part is what's doing a lot of extra lifting there.

Do you know of any good evaluations between using vector space data and other methods for summarization?

[meeper16]

Word2Vec was a fork or based on a more exhuastive vector space approach here https://www.kaggle.com/c/word2vec-nlp-tutorial/forums/t/1234...

I've compared the summarization to others like OTS http://libots.sourceforge.net/ which I believe strictly relies on TF-IDF and it seems better and allows for context to control the summarization.

Other similar approaches might be based on Latent Semantic Analysis, Latent Semantic Indexing or LDA.

[gibrown]

Thanks for the links!