def test_corpus_summarization(self):
pre_path = os.path.join(os.path.dirname(__file__), 'test_data')
with utils.smart_open(os.path.join(pre_path, "mihalcea_tarau.txt"), mode="r") as f:
text = f.read()
# Generate the corpus.
sentences = text.split("\n")
tokens = [sentence.split() for sentence in sentences]
dictionary = Dictionary(tokens)
corpus = [dictionary.doc2bow(sentence_tokens) for sentence_tokens in tokens]
# Extract the most important documents.
selected_documents = summarize_corpus(corpus)
# They are compared to the method reference.
with utils.smart_open(os.path.join(pre_path, "mihalcea_tarau.summ.txt"), mode="r") as f:
summary = f.read()
summary = summary.split('\n')
# Each sentence in the document selection has to be in the model summary.
for doc_number, document in enumerate(selected_documents):
# Retrieves all words from the document.
words = [dictionary[token_id] for (token_id, count) in document]
# Asserts that all of them are in a sentence from the model reference.
self.assertTrue(any(all(word in sentence for word in words)) for sentence in summary)
def test_corpus_summarization(self):
text = self._get_text_from_test_data("mihalcea_tarau.txt")
# Generate the corpus.
sentences = text.split("\n")
tokens = [sentence.split() for sentence in sentences]
dictionary = Dictionary(tokens)
corpus = [
dictionary.doc2bow(sentence_tokens) for sentence_tokens in tokens
]
# Extract the most important documents.
selected_documents = summarize_corpus(corpus)
# They are compared to the method reference.
summary = self._get_text_from_test_data("mihalcea_tarau.summ.txt")
summary = summary.split('\n')
# Each sentence in the document selection has to be in the model summary.
for doc_number, document in enumerate(selected_documents):
# Retrieves all words from the document.
words = [dictionary[token_id] for (token_id, count) in document]
# Asserts that all of them are in a sentence from the model reference.
self.assertTrue(
any(all(word in sentence for word in words))
for sentence in summary)
def test_low_distinct_words_corpus_summarization_is_empty_list(self):
text = self._get_text_from_test_data("testlowdistinctwords.txt")
# Generate the corpus.
sentences = text.split("\n")
tokens = [sentence.split() for sentence in sentences]
dictionary = Dictionary(tokens)
corpus = [dictionary.doc2bow(sentence_tokens) for sentence_tokens in tokens]
self.assertEquals(summarize_corpus(corpus), [])
def test_low_distinct_words_corpus_summarization_is_empty_list(self):
text = self._get_text_from_test_data("testlowdistinctwords.txt")
# Generate the corpus.
sentences = text.split("\n")
tokens = [sentence.split() for sentence in sentences]
dictionary = Dictionary(tokens)
corpus = [dictionary.doc2bow(sentence_tokens) for sentence_tokens in tokens]
self.assertEqual(summarize_corpus(corpus), [])
def test_corpus_summarization_is_not_empty_list_on_short_input_text(self):
text = self._get_text_from_test_data("testsummarization_unrelated.txt")
# Keeps the first 8 sentences to make the text shorter.
sentences = text.split('\n')[:8]
# Generate the corpus.
tokens = [sentence.split() for sentence in sentences]
dictionary = Dictionary(tokens)
corpus = [dictionary.doc2bow(sentence_tokens) for sentence_tokens in tokens]
self.assertNotEqual(summarize_corpus(corpus), [])
def test_corpus_summarization_is_not_empty_list_on_short_input_text(self):
text = self._get_text_from_test_data("testsummarization_unrelated.txt")
# Keeps the first 8 sentences to make the text shorter.
sentences = text.split('\n')[:8]
# Generate the corpus.
tokens = [sentence.split() for sentence in sentences]
dictionary = Dictionary(tokens)
corpus = [dictionary.doc2bow(sentence_tokens) for sentence_tokens in tokens]
self.assertNotEqual(summarize_corpus(corpus), [])
def test_low_distinct_words_corpus_summarization_is_none(self):
pre_path = os.path.join(os.path.dirname(__file__), 'test_data')
with utils.smart_open(os.path.join(pre_path, "testlowdistinctwords.txt"), mode="r") as f:
text = f.read()
# Generate the corpus.
sentences = text.split("\n")
tokens = [sentence.split() for sentence in sentences]
dictionary = Dictionary(tokens)
corpus = [dictionary.doc2bow(sentence_tokens) for sentence_tokens in tokens]
self.assertTrue(summarize_corpus(corpus) is None)
def test_corpus_summarization_raises_exception_on_short_input_text(self):
pre_path = os.path.join(os.path.dirname(__file__), 'test_data')
with utils.smart_open(os.path.join(pre_path, "testsummarization_unrelated.txt"), mode="r") as f:
text = f.read()
# Keeps the first 8 sentences to make the text shorter.
sentences = text.split('\n')[:8]
# Generate the corpus.
tokens = [sentence.split() for sentence in sentences]
dictionary = Dictionary(tokens)
corpus = [dictionary.doc2bow(sentence_tokens) for sentence_tokens in tokens]
self.assertTrue(summarize_corpus(corpus) is not None)
def test_corpus_summarization_ratio(self):
text = self._get_text_from_test_data("mihalcea_tarau.txt")
# Generate the corpus.
sentences = text.split('\n')
tokens = [sentence.split() for sentence in sentences]
dictionary = Dictionary(tokens)
corpus = [dictionary.doc2bow(sentence_tokens) for sentence_tokens in tokens]
# Makes summaries of the text using different ratio parameters.
for x in range(1, 10):
ratio = x / float(10)
selected_docs = summarize_corpus(corpus, ratio=ratio)
expected_summary_length = int(len(corpus) * ratio)
self.assertEqual(len(selected_docs), expected_summary_length)
def test_corpus_summarization_ratio(self):
text = self._get_text_from_test_data("mihalcea_tarau.txt")
# Generate the corpus.
sentences = text.split('\n')
tokens = [sentence.split() for sentence in sentences]
dictionary = Dictionary(tokens)
corpus = [dictionary.doc2bow(sentence_tokens) for sentence_tokens in tokens]
# Makes summaries of the text using different ratio parameters.
for x in range(1, 10):
ratio = x / float(10)
selected_docs = summarize_corpus(corpus, ratio=ratio)
expected_summary_length = int(len(corpus) * ratio)
self.assertEqual(len(selected_docs), expected_summary_length)
def test_corpus_summarization_ratio(self):
pre_path = os.path.join(os.path.dirname(__file__), 'test_data')
with utils.smart_open(os.path.join(pre_path, "mihalcea_tarau.txt"), mode="r") as f:
text = f.read()
# Generate the corpus.
sentences = text.split('\n')
tokens = [sentence.split() for sentence in sentences]
dictionary = Dictionary(tokens)
corpus = [dictionary.doc2bow(sentence_tokens) for sentence_tokens in tokens]
# Makes summaries of the text using different ratio parameters.
for x in range(1, 10):
ratio = x / float(10)
selected_docs = summarize_corpus(corpus, ratio=ratio)
expected_summary_length = int(len(corpus) * ratio)
self.assertEqual(len(selected_docs), expected_summary_length)
def test_corpus_summarization(self):
text = self._get_text_from_test_data("mihalcea_tarau.txt")
# Generate the corpus.
sentences = text.split("\n")
tokens = [sentence.split() for sentence in sentences]
dictionary = Dictionary(tokens)
corpus = [dictionary.doc2bow(sentence_tokens) for sentence_tokens in tokens]
# Extract the most important documents.
selected_documents = summarize_corpus(corpus)
# They are compared to the method reference.
summary = self._get_text_from_test_data("mihalcea_tarau.summ.txt")
summary = summary.split('\n')
# Each sentence in the document selection has to be in the model summary.
for doc_number, document in enumerate(selected_documents):
# Retrieves all words from the document.
words = [dictionary[token_id] for (token_id, count) in document]
# Asserts that all of them are in a sentence from the model reference.
self.assertTrue(any(all(word in sentence for word in words)) for sentence in summary)
def test_empty_corpus_summarization_is_none(self):
self.assertTrue(summarize_corpus([]) is None)
def test_empty_corpus_summarization_is_empty_list(self):
self.assertEqual(summarize_corpus([]), [])
def test_empty_corpus_summarization_is_empty_list(self):
self.assertEqual(summarize_corpus([]), [])
