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The list of "things that are useful when describing noun phrases" cannot be complete, because there is no one list of what is relevant to every analysis. That being said, here are a few relevant things: What is obligatorily marked? This can include: plurality, case, definiteness Where does marking occur? As an affix to the head, on every word in the phrase? ...


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A list of extractive summary corpora can be found over on ResearchGate. Additionally, a quick search revealed multiple papers on supervised extractive summarization (for example, this one), using the DUC or TAC datasets. Although I've not personally worked with these datasets, if they're used in supervised learning approaches they should contain the ...


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You do not have to do much if you are going to make an Extractive summarization. Do stop-word Removal Do stemming (optional) Count all words occurrences Extract sentence(s) that contains the word with most frequency in the text This is the basic of text summarization which suits for emails very well, I guess.


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Here's a tool I happened upon recently: Context-controllable Content Summarization And this looks to be related background information: Generating content summarizations similar to the way a human might do so Word2Vec is based on an approach from Lawrence Berkeley National Lab https://code.google.com/p/word2vec/


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