Detecting boilerplate in text samples
This might get you started. Phrase length is determined by the range() function. Basically this tokenizes and creates n-grams. Then it counts each token. Tokens with a high mean over all documents (occurs often across documents) is printed out in the last line.

from sklearn.feature_extraction.text import CountVectorizer
import numpy as np
import nltk

text = """DESCRIPTION PROVIDED BY AUTHOR: The goal of my a...
Author provided: The goal of my b...
The goal of my c... END OF TRANSCRIPT
The goal of my d... END SPONSORED BY COMPANY XYZ
The goal of my e... SPONSORED: COMPANY XYZ All rights reserved date: 10/21
"""

def todocuments(lines):
    for line in lines:
        words = line.lower().split(' ')
        doc = ""
        for n in range(3, 6):
            ts = nltk.ngrams(words, n)
            for t in ts: doc = doc + " " + str.join('_', t) 
        yield doc

cv = CountVectorizer(min_df=.5)

fit = cv.fit_transform(todocuments(text.splitlines()))
vocab_idx = {b: a for a, b in cv.vocabulary_.items()}

means = fit.mean(axis=0)
arr = np.squeeze(np.asarray(means))
[vocab_idx[idx] for idx in np.where(arr &gt; .95)[0]]
# ['goal_of_my', 'the_goal_of', 'the_goal_of_my']