def start_run(processID, POPU_SIZE, MAX_GEN, CROSS_RATE, MUTATE_RATE,
sub_stories, save_path, order_params):
for example in sub_stories:
start_time = time.time()
raw_sents = re.split("\n\n", example[0])[1].split(' . ')
title = re.split("\n\n", example[0])[0]
abstract = re.split("\n\n", example[0])[2]
#remove too short sentences
df = pd.DataFrame(raw_sents, columns=['raw'])
df['preprocess_raw'] = df['raw'].apply(lambda x: clean_text(x))
newdf = df.loc[(df['preprocess_raw'] != 'None')]
raw_sentences = newdf['preprocess_raw'].values.tolist()
if len(raw_sentences) == 0:
continue
preprocessed_sentences = []
for raw_sent in raw_sentences:
preprocessed_sent = preprocess_raw_sent(raw_sent)
preprocessed_sentences.append(preprocessed_sent)
preprocessed_abs_sentences_list = []
raw_abs_sent_list = abstract.split(' . ')
for abs_sent in raw_abs_sent_list:
preprocessed_abs_sent = preprocess_raw_sent(abs_sent)
preprocessed_abs_sentences_list.append(preprocessed_abs_sent)
preprocessed_abs_sentences = (
" ").join(preprocessed_abs_sentences_list)
if len(preprocessed_sentences) < 7 or len(
preprocessed_abs_sentences_list) < 3:
continue
rougeforsentences = evaluate_rouge(raw_sentences, abstract)
print("Done preprocessing!")
print('time for processing', time.time() - start_time)
if len(preprocessed_sent) < 4:
NUM_PICKED_SENTS = len(preprocessed_sentences)
else:
NUM_PICKED_SENTS = 4
# DONE!
Solver = Summerizer(title, preprocessed_sentences, raw_sentences,
POPU_SIZE, MAX_GEN, CROSS_RATE, MUTATE_RATE,
NUM_PICKED_SENTS, rougeforsentences, abstract,
order_params)
best_individual = Solver.solve()
file_name = os.path.join(save_path, example[1])
print(file_name)
if best_individual is None:
solution_for_exception(rougeforsentences, raw_sentences, file_name)
else:
print(best_individual)
Solver.show(best_individual, file_name)
def start_run(processID, POPU_SIZE, MAX_GEN, CROSS_RATE, MUTATE_RATE,
sub_stories, save_path, order_params):
for example in sub_stories:
start_time = time.time()
raw_sents = re.split("\n\n", example[0])[1].split(' . ')
title = re.split("\n\n", example[0])[0]
abstract = re.split("\n\n", example[0])[2]
#remove too short sentences
df = pd.DataFrame(raw_sents, columns=['raw'])
df['preprocess_raw'] = df['raw'].apply(lambda x: clean_text(x))
newdf = df.loc[(df['preprocess_raw'] != 'None')]
raw_sentences = newdf['preprocess_raw'].values.tolist()
if len(raw_sentences) == 0:
continue
preprocessed_sentences = []
for raw_sent in raw_sentences:
preprocessed_sent = preprocess_raw_sent(raw_sent)
preprocessed_sentences.append(preprocessed_sent)
preprocessed_abs_sentences_list = []
raw_abs_sent_list = abstract.split(' . ')
for abs_sent in raw_abs_sent_list:
preprocessed_abs_sent = preprocess_raw_sent(abs_sent)
preprocessed_abs_sentences_list.append(preprocessed_abs_sent)
preprocessed_abs_sentences = (
" ").join(preprocessed_abs_sentences_list)
if len(preprocessed_sentences) < 7 or len(
preprocessed_abs_sentences_list) < 3:
continue
rougeforsentences = evaluate_rouge(raw_sentences, abstract)
rank_rougeforsentences = sorted(rougeforsentences,
key=lambda x: x[1],
reverse=True)
length_of_summary = int(0.2 * len(raw_sentences))
rank_rouge = rank_rougeforsentences[:length_of_summary]
rank_rouge = sorted(rank_rouge, key=lambda x: x[2], reverse=False)
print("Done preprocessing!")
print('time for processing', time.time() - start_time)
file_name = os.path.join(save_path, example[1])
f = open(file_name, 'w', encoding='utf-8')
for sent in rank_rouge:
f.write(sent[0] + ' ')
f.close()
def start_run(processID, POPU_SIZE, MAX_GEN, CROSS_RATE, MUTATE_RATE, sub_stories, save_path, order_params):
for example in sub_stories:
file_name = os.path.join(save_path, example[1])
start_time = time.time()
raw_sents = re.split(" . ", example[0])
#remove too short sentences
df = pd.DataFrame(raw_sents, columns =['raw'])
df['preprocess_raw'] = df['raw'].apply(lambda x: clean_text(x))
newdf = df.loc[(df['preprocess_raw'] != 'None')]
raw_sentences = newdf['preprocess_raw'].values.tolist()
if len(raw_sentences) < 5:
continue
preprocessed_sentences = []
for raw_sent in raw_sentences:
preprocessed_sent = preprocess_raw_sent(raw_sent)
preprocessed_sentences.append(preprocessed_sent)
if len(preprocessed_sentences) < 10:
solution_for_exception(raw_sentences, file_name)
title = preprocessed_sentences[0]
vectorizer = TfidfVectorizer()
vectors = vectorizer.fit_transform(preprocessed_sentences)
feature_names = vectorizer.get_feature_names()
dense = vectors.todense()
list_sentences_frequencies = dense.tolist()
# df_tfidf = pd.DataFrame(list_sentences_frequencies, columns=feature_names)
title_vector = list_sentences_frequencies[0]
#tfidf for document and abstract
document = [(" ").join(preprocessed_sentences)]
vector_doc = vectorizer.fit_transform(document)
dense_doc = vector_doc.todense()
document_vector = dense_doc.tolist()[0]
number_of_nouns = count_noun(raw_sentences, option= True)
simWithTitle = sim_with_title(list_sentences_frequencies, title_vector)
sim2sents = sim_2_sent(list_sentences_frequencies)
simWithDoc = sim_with_doc(list_sentences_frequencies, document_vector)
NUM_PICKED_SENTS = 4
print("Done preprocessing!")
print('time for processing', time.time() - start_time)
Solver = Summerizer(title, preprocessed_sentences, raw_sentences, POPU_SIZE, MAX_GEN, CROSS_RATE, MUTATE_RATE, NUM_PICKED_SENTS, simWithTitle, simWithDoc, sim2sents, number_of_nouns, order_params)
best_individual = Solver.solve()
print(file_name)
if best_individual is None:
solution_for_exception(raw_sentences, file_name)
else:
print(best_individual)
Solver.show(best_individual, file_name)
def sim_with_title_of_paragraph(document):
paragraphs = document.split('\n')
sim_header = []
raw_sents = []
preprocessed_sents = []
for para in paragraphs:
raw = para.split(' . ')
df = pd.DataFrame(raw, columns=['raw'])
df['preprocess_raw'] = df['raw'].apply(lambda x: clean_text(x))
newdf = df.loc[(df['preprocess_raw'] != 'None')]
newdf['preprocessed_raw'] = df['preprocess_raw'].apply(
lambda x: preprocess_raw_sent(x))
raw_sentences = newdf['preprocess_raw'].values.tolist()
preprocessed_sentences = newdf['preprocessed_raw'].values.tolist()
# preprocessed_sentences = []
# for raw_sent in raw_sentences:
# preprocessed_sent = preprocess_raw_sent(raw_sent)
# preprocessed_sentences.append(preprocessed_sent)
raw_sents.extend(raw_sentences)
preprocessed_sents.extend(preprocessed_sentences)
#similar with header of paragraph
vectorizer = TfidfVectorizer()
vectors = vectorizer.fit_transform(preprocessed_sentences)
feature_names = vectorizer.get_feature_names()
dense = vectors.todense()
denselist = dense.tolist()
df_tfidf = pd.DataFrame(denselist, columns=feature_names)
simWithTitle = sim_with_title(denselist, denselist[0])
sim_header.extend(simWithTitle)
del df
del newdf
return raw_sents, preprocessed_sents, sim_header
def start_run(processID, POPU_SIZE, MAX_GEN, CROSS_RATE, MUTATE_RATE,
sub_stories, save_path, order_params):
for example in sub_stories:
start_time = time.time()
raw_doc = re.split("\n\n", example[0])[1]
title = re.split("\n\n", example[0])[0]
abstract = re.split("\n\n", example[0])[2]
#remove too short sentences
# df = pd.DataFrame(raw_sents, columns =['raw'])
# df['preprocess_raw'] = df['raw'].apply(lambda x: clean_text(x))
# newdf = df.loc[(df['preprocess_raw'] != 'None')]
# raw_sentences = newdf['preprocess_raw'].values.tolist()
# preprocessed_sentences = []
# for raw_sent in raw_sentences:
# preprocessed_sent = preprocess_raw_sent(raw_sent)
# preprocessed_sentences.append(preprocessed_sent)
raw_sentences, preprocessed_sentences, simWithTitle = sim_with_title_of_paragraph(
raw_doc)
if len(raw_sentences) == 0:
continue
preprocessed_abs_sentences_list = []
raw_abs_sent_list = abstract.split(' . ')
for abs_sent in raw_abs_sent_list:
preprocessed_abs_sent = preprocess_raw_sent(abs_sent)
preprocessed_abs_sentences_list.append(preprocessed_abs_sent)
if len(preprocessed_abs_sentences_list) < 4 or len(
preprocessed_sentences) < 7:
continue
preprocessed_abs_sentences = (
" ").join(preprocessed_abs_sentences_list)
#tfidf for sentences
bodyandtitle = preprocessed_sentences.copy()
bodyandtitle.append(preprocess_raw_sent(title.lower()))
full_text = preprocessed_sentences.copy()
full_text.append(preprocessed_abs_sentences)
full_text.append(preprocess_raw_sent(title.lower()))
vectorizer = TfidfVectorizer()
vectors = vectorizer.fit_transform(full_text)
feature_names = vectorizer.get_feature_names()
dense = vectors.todense()
denselist = dense.tolist()
df_tfidf = pd.DataFrame(denselist, columns=feature_names)
title_vector = denselist[-1]
#tfidf for document and abstract
document = [(" ").join(bodyandtitle), preprocessed_abs_sentences]
vector_doc = vectorizer.fit_transform(document)
dense_doc = vector_doc.todense()
document_vector = dense_doc.tolist()[0]
abstract_vector = dense_doc.tolist()[1]
list_sentences_frequencies = denselist[:-2]
# number_of_nouns = count_noun(preprocessed_sentences, option = True)
number_of_nouns = 0
# simWithTitle = sim_with_title(list_sentences_frequencies, title_vector)
sim2sents = sim_2_sent(list_sentences_frequencies)
simWithDoc = sim_with_doc(list_sentences_frequencies, document_vector)
simWithAbs = sim_with_doc(list_sentences_frequencies, abstract_vector)
rougeforsentences = evaluate_rouge(raw_sentences, abstract)
print("Done preprocessing!")
print('time for processing', time.time() - start_time)
if len(preprocessed_sentences) < 4:
NUM_PICKED_SENTS = len(preprocessed_sentences)
else:
NUM_PICKED_SENTS = 4
# DONE!
Solver = Summerizer(title, preprocessed_sentences, raw_sentences,
POPU_SIZE, MAX_GEN, CROSS_RATE, MUTATE_RATE,
NUM_PICKED_SENTS, simWithTitle, simWithDoc,
sim2sents, number_of_nouns, simWithAbs,
rougeforsentences, order_params)
best_individual = Solver.solve()
file_name = os.path.join(save_path, example[1])
if best_individual is None:
print('No solution.')
else:
print(file_name)
print(best_individual)
Solver.show(best_individual, file_name)
def main():
# Setting Variables
POPU_SIZE = 30
MAX_GEN = 20
CROSS_RATE = 0.8
MUTATE_RATE = 0.4
NUM_PICKED_SENTS = 4
directory = 'stories'
save_path = 'hyp'
print("Setting: ")
print("POPULATION SIZE: {}".format(POPU_SIZE))
print("MAX NUMBER OF GENERATIONS: {}".format(MAX_GEN))
print("CROSSING RATE: {}".format(CROSS_RATE))
print("MUTATION SIZE: {}".format(MUTATE_RATE))
# list of documents
stories = load_docs(directory)
start_time = time.time()
for example in stories:
try:
raw_sents = example[0].split(" . ")
print("Preprocessing ", example[1])
sentences = []
sentences_for_NNP = []
df = pd.DataFrame(raw_sents, columns=['raw'])
df['preprocess_raw'] = df['raw'].apply(lambda x: clean_text(x))
newdf = df.loc[(df['preprocess_raw'] != 'None')]
raw_sentences = newdf['preprocess_raw'].values.tolist()
for raw_sent in raw_sentences:
sent = preprocess_raw_sent(raw_sent)
sent_tmp = preprocess_numberOfNNP(raw_sent)
# print(f'time-preprocess_numberOfNNP = {time.time() - time_2} s')
sentences.append(sent)
sentences_for_NNP.append(sent_tmp)
title_raw = raw_sentences[0]
title = preprocess_raw_sent(title_raw)
number_of_nouns = count_noun(sentences_for_NNP)
simWithTitle = sim_with_title(sentences, title)
sim2sents = sim_2_sent(sentences)
simWithDoc = []
for sent in sentences:
simWithDoc.append(sim_with_doc(sent, sentences))
print("Done preprocessing!")
# DONE!
Solver = Summerizer(title, sentences, raw_sentences, POPU_SIZE,
MAX_GEN, CROSS_RATE, MUTATE_RATE,
NUM_PICKED_SENTS, simWithTitle, simWithDoc,
sim2sents, number_of_nouns)
best_individual = Solver.PSO()
file_name = os.path.join(save_path, example[1])
if best_individual is None:
print('No solution.')
else:
print(file_name)
print(best_individual)
Solver.show(best_individual, file_name)
except Exception as e:
print(example[1])
print("type error: " + str(e))
print("--- %s mins ---" % ((time.time() - start_time) /
(60.0 * len(stories))))
def start_run(processID, POPU_SIZE, MAX_GEN, CROSS_RATE, MUTATE_RATE,
sub_stories, save_path, order_params, scheme):
for example in sub_stories:
start_time = time.time()
raw_sents = re.split("\n", example[0])
df = pd.DataFrame(raw_sents, columns=['raw'])
df['preprocess_raw'] = df['raw'].apply(lambda x: clean_text(x))
newdf = df.loc[(df['preprocess_raw'] != 'None')]
raw_sentences = newdf['preprocess_raw'].values.tolist()
if len(raw_sentences) == 0:
continue
title_raw = raw_sentences[0]
sentences = []
sentences_for_NNP = []
for raw_sent in raw_sentences:
sent = preprocess_raw_sent(raw_sent)
# sent_tmp = preprocess_numberOfNNP(raw_sent)
sent_tmp = preprocess_raw_sent(raw_sent, True)
if len(sent.split(' ')) < 2:
raw_sentences.remove(raw_sent)
else:
sentences.append(sent)
sentences_for_NNP.append(sent_tmp)
title = preprocess_raw_sent(title_raw)
list_sentences_frequencies = word_frequencies(sentences, title)
number_of_nouns = count_noun(sentences_for_NNP)
simWithTitle = sim_with_title(list_sentences_frequencies)
sim2sents = sim_2_sent(list_sentences_frequencies)
simWithDoc = []
# for sent in sentences:
for i in range(len(sentences)):
simWithDoc.append(
sim_with_doc(list_sentences_frequencies, index_sentence=i))
# POPU_SIZE = 40
if len(sentences) < 20:
MAX_GEN = 20
elif len(sentences) < 50:
MAX_GEN = 50
else:
MAX_GEN = 80
print("POPULATION SIZE: {}".format(POPU_SIZE))
print("MAX NUMBER OF GENERATIONS: {}".format(MAX_GEN))
print("Done preprocessing!")
# DONE!
print('time for processing', time.time() - start_time)
if len(sentences) < 4:
NUM_PICKED_SENTS = len(sentences)
else:
NUM_PICKED_SENTS = 4
MinLT = 1
MaxLT = 7
Solver = Summerizer(title, sentences, raw_sentences, POPU_SIZE,
MAX_GEN, CROSS_RATE, MUTATE_RATE, NUM_PICKED_SENTS,
simWithTitle, simWithDoc, sim2sents,
number_of_nouns, order_params, MinLT, MaxLT,
scheme)
best_individual = Solver.solve()
file_name = os.path.join(save_path, example[1])
if best_individual is None:
print('No solution.')
else:
print(file_name)
print(best_individual)
Solver.show(best_individual, file_name)
def start_run(processID, sub_stories, save_path, word_embeddings):
for example in sub_stories:
start_time = time.time()
raw_sents = re.split("\n\n", example[0])[1].split(' . ')
title = re.split("\n\n", example[0])[0]
abstract = re.split("\n\n", example[0])[2]
#remove too short sentences
df = pd.DataFrame(raw_sents, columns=['raw'])
df['preprocess_raw'] = df['raw'].apply(lambda x: clean_text(x))
newdf = df.loc[(df['preprocess_raw'] != 'None')]
raw_sentences = newdf['preprocess_raw'].values.tolist()
if len(raw_sentences) == 0:
continue
preprocessed_sentences = []
for raw_sent in raw_sentences:
preprocessed_sent = preprocess_raw_sent(raw_sent)
preprocessed_sentences.append(preprocessed_sent)
preprocessed_abs_sentences_list = []
raw_abs_sent_list = abstract.split(' . ')
for abs_sent in raw_abs_sent_list:
preprocessed_abs_sent = preprocess_raw_sent(abs_sent)
preprocessed_abs_sentences_list.append(preprocessed_abs_sent)
preprocessed_abs_sentences = (
" ").join(preprocessed_abs_sentences_list)
if len(preprocessed_sentences) < 7 or len(
preprocessed_abs_sentences_list) < 3:
continue
sentences = preprocessed_sentences.copy()
sentence_vectors = []
for i in sentences:
if len(i) != 0:
v = sum([
word_embeddings.get(w, np.zeros((50, )))
for w in i.split()
]) / (len(i.split()) + 0.001)
else:
v = np.zeros((50, ))
sentence_vectors.append(v)
sim_mat = np.zeros([len(sentences), len(sentences)])
for i in range(len(sentences)):
for j in range(len(sentences)):
if i != j:
sim_mat[i][j] = cosine_similarity(
sentence_vectors[i].reshape(1, 50),
sentence_vectors[j].reshape(1, 50))[0, 0]
nx_graph = nx.from_numpy_array(sim_mat)
try:
scores = nx.pagerank(nx_graph) # score of all sentences in article
except Exception:
continue
scores_with_sentences = []
for i in range(len(raw_sentences)):
tmp = (raw_sentences[i], scores[i], i)
scores_with_sentences.append(tmp)
rank_scores_with_sentences = sorted(scores_with_sentences,
key=lambda x: x[1],
reverse=True)
length_of_summary = int(0.2 * len(raw_sentences))
rank_text = rank_scores_with_sentences[:length_of_summary]
rank_text = sorted(rank_text, key=lambda x: x[2], reverse=False)
print("Done preprocessing!")
print('time for processing', time.time() - start_time)
file_name = os.path.join(save_path, example[1])
f = open(file_name, 'w', encoding='utf-8')
for sent in rank_text:
f.write(sent[0] + ' ')
f.close()
def start_run(processID, POPU_SIZE, MAX_GEN, CROSS_RATE, MUTATE_RATE,
sub_stories, save_path, order_params):
# def start_run(POPU_SIZE, MAX_GEN, CROSS_RATE, MUTATE_RATE, sub_stories, save_path, order_params):
for example in sub_stories:
start_time = time.time()
# raw_sentences = re.split("\n\s+", example[0])
raw_sents = re.split("\n", example[0])
df = pd.DataFrame(raw_sents, columns=['raw'])
df['preprocess_raw'] = df['raw'].apply(lambda x: clean_text(x))
newdf = df.loc[(df['preprocess_raw'] != 'None')]
raw_sentences = newdf['preprocess_raw'].values.tolist()
if len(raw_sentences) == 0:
continue
# print('raw', len(raw_sentences), stories.index(example))
title_raw = raw_sentences[0]
# Preprocessing
# print("Preprocessing...")
sentences = []
sentences_for_NNP = []
for raw_sent in raw_sentences:
sent = preprocess_raw_sent(raw_sent)
# sent_tmp = preprocess_numberOfNNP(raw_sent)
sent_tmp = preprocess_raw_sent(raw_sent, True)
if len(sent.split(' ')) < 2:
raw_sentences.remove(raw_sent)
else:
sentences.append(sent)
sentences_for_NNP.append(sent_tmp)
title = preprocess_raw_sent(title_raw)
list_sentences_frequencies = word_frequencies(sentences, title)
number_of_nouns = count_noun(sentences_for_NNP)
simWithTitle = sim_with_title(list_sentences_frequencies)
sim2sents = sim_2_sent(list_sentences_frequencies)
simWithDoc = []
# for sent in sentences:
for i in range(len(sentences)):
simWithDoc.append(
sim_with_doc(list_sentences_frequencies, index_sentence=i))
print("Done preprocessing!")
# DONE!
print('time for processing', time.time() - start_time)
if len(sentences) < 4:
NUM_PICKED_SENTS = len(sentences)
else:
# NUM_PICKED_SENTS = x
# NUM_PICKED_SENTS = int(len(sentences)*0.2)
NUM_PICKED_SENTS = 4
Solver = Summerizer(title, sentences, raw_sentences, POPU_SIZE,
MAX_GEN, CROSS_RATE, MUTATE_RATE, NUM_PICKED_SENTS,
simWithTitle, simWithDoc, sim2sents,
number_of_nouns, order_params)
best_individual = Solver.PSO()
file_name = os.path.join(save_path, example[1])
if best_individual is None:
print('No solution.')
else:
print(file_name)
print(best_individual)
Solver.show(best_individual, file_name)