### fill the gap (one line) ###
# apply the 'get_word_att_coeffs' model over all the sentences in 'my_review_tensor', and store the results as 'word_coeffs'
word_coeffs = TimeDistributed(get_word_att_coeffs)(my_review_tensor)
word_coeffs = K.eval(
word_coeffs
) # shape = (1, 7, 30, 1): (batch size, nb of sents in doc, nb of words per sent, coeff)
word_coeffs = word_coeffs[
0, :, :, 0] # shape = (7, 30) (coeff for each word in each sentence)
word_coeffs = sent_coeffs * word_coeffs # re-weigh according to sentence importance
word_coeffs = np.round((word_coeffs * 100).astype(np.float64), 2)
word_coeffs_list = word_coeffs.tolist()
# match text and coefficients
text_word_coeffs = [
list(zip(words, word_coeffs_list[idx][:len(words)]))
for idx, words in enumerate(my_review_text)
]
for sent in text_word_coeffs:
[print(elt) for elt in sent]
print('= = = =')
# sort words by importance within each sentence
text_word_coeffs_sorted = [
sorted(elt, key=operator.itemgetter(1), reverse=True)
for elt in text_word_coeffs
