binary document classifcation
I generally recommend using Scikit as Slater suggested. Its more scalable than NLTK. For this task using Naive Bayes Classifier or Support Vector Machine is your best bet. You are dealing with binary classification so you don't have multi classes. 
As for the features that you should extract, try unigrams, bigrams, trigrams, and TFIDF features. Also, LDA might turn out useful, but start with the easier ones such as unigrams. 
This also depends on the type and length of texts you are dealing with. Document classification has been around for more than a decade and there are so many good papers that you could find useful.
Let me know if you have any further questions.
I know this is a very vague question but I'm trying to figure out the best way to do document classification. I have two sets training and testing. The training set is a set of documents each labeled 1 or 0. The documents are labeled 1 if is it a informative summary and a 0 if it is not. I'm trying to create a supervised classifier. I can't decide which nlp toolkit to use. I'm thinking nltk. Do you have any suggestions? I have to write the classifier in python. Also any specific types of classifiers. I've been doing research but can't seem to get a good answer.