def get_importances(model, batch, enc_states, vocab, sess, hps):
if FLAGS.pg_mmr:
enc_sentences, enc_tokens = batch.tokenized_sents[
0], batch.word_ids_sents[0]
if FLAGS.importance_fn == 'oracle':
human_tokens = get_tokens_for_human_summaries(
batch,
vocab) # list (of 4 human summaries) of list of token ids
metric = 'recall'
importances_hat = rouge_l_similarity(enc_tokens,
human_tokens,
vocab,
metric=metric)
elif FLAGS.importance_fn == 'svr':
if FLAGS.importance_fn == 'svr':
with open(os.path.join(FLAGS.actual_log_root, 'svr.pickle'),
'rb') as f:
svr_model = cPickle.load(f)
enc_sent_indices = importance_features.get_sent_indices(
enc_sentences, batch.doc_indices[0])
sent_representations_separate = importance_features.get_separate_enc_states(
model, sess, enc_sentences, vocab, hps)
importances_hat = get_svr_importances(
enc_states[0], enc_sentences, enc_sent_indices, svr_model,
sent_representations_separate)
elif FLAGS.importance_fn == 'tfidf':
importances_hat = get_tfidf_importances(batch.raw_article_sents[0])
importances = util.special_squash(importances_hat)
else:
importances = None
return importances
def get_ROUGE_Ls(art_oovs, all_original_abstracts_sents, vocab, enc_tokens):
human_tokens = get_tokens_for_human_summaries(
art_oovs, all_original_abstracts_sents, vocab,
split_sents=False) # list (of 4 human summaries) of list of token ids
metric = 'recall'
importances_hat = util.rouge_l_similarity(enc_tokens,
human_tokens,
vocab,
metric=metric)
importances = util.special_squash(importances_hat)
return importances, importances_hat