Issue 80: content_selection.py - added BLR formula variations & necessary parameters

[sladymon]

For select_content():

• Added parameters with default values. NOTE: I will need to add these to text_summarizer.py later when no one else is touching the file. ALSO NOTE: I will change the defaults for these parameter values after tuning on training data
• Added doc string explaining what each of the parameters is
• Changed the functions it calls to pass in the necessary parameters for each

For _build_sim_matrix():

• Fixed bug where sentences were being added for cosine similarity without a threshold check
• Added ability to use cosine similarity OR normalized generative probability, which adds only the top K nodes' similarities

For _build_bias_vec():

• Added ability to use relevance OR generative probability OR cosine similarity for the bias weighting.

New formulas functions:

• _calc_relevance(): calculates bias relevance using formula from Otterbacher (2005)
• _calc_smoothed_mle(), _calc_gen_prob(), _calc_norm_gen_prob(): formulas used to calculate generative & normalized generative probabilities from Otterbacher (2009)

Other Notes:

• _cosine_similarity() now runs on the raw_counts version of tf-idf, which does not change its output at all (cosine normalizes, so the previously normalized tf-idf didn't run any differently).

[aventon1]

Good params, different combinations of bias_formula and intersent_formula improve our scores! :)