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Add verification when summarize_corpus returns null. Fix #1531. #1570

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merged 5 commits into from
Sep 18, 2017
Merged

Add verification when summarize_corpus returns null. Fix #1531. #1570

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d200a5d
Avoid "NoneType is not iterable..." error for few documents in corpus.
fbarrios Sep 5, 2017
3b4982e
Fix comment.
fbarrios Sep 5, 2017
78147bc
Adding relevant test.
fbarrios Sep 5, 2017
e3a1d88
Fixed return types on summarization border cases:
fbarrios Sep 9, 2017
91c8142
Replace `is` to `==`
menshikh-iv Sep 14, 2017
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14 changes: 10 additions & 4 deletions gensim/summarization/summarizer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -147,7 +147,7 @@ def summarize_corpus(corpus, ratio=0.2):
# If the corpus is empty, the function ends.
if len(corpus) == 0:
logger.warning("Input corpus is empty.")
return
return []

# Warns the user if there are too few documents.
if len(corpus) < INPUT_MIN_LENGTH:
Expand All @@ -157,10 +157,11 @@ def summarize_corpus(corpus, ratio=0.2):
_set_graph_edge_weights(graph)
_remove_unreachable_nodes(graph)

# Cannot calculate eigenvectors if number of unique words in text < 3. Warns user to add more text. The function ends.
# Cannot calculate eigenvectors if number of unique documents in corpus < 3.
# Warns user to add more text. The function ends.
if len(graph.nodes()) < 3:
logger.warning("Please add more sentences to the text. The number of reachable nodes is below 3")
return
return []

pagerank_scores = _pagerank(graph)

Expand Down Expand Up @@ -197,7 +198,7 @@ def summarize(text, ratio=0.2, word_count=None, split=False):
# If no sentence could be identified, the function ends.
if len(sentences) == 0:
logger.warning("Input text is empty.")
return
return [] if split else u""

# If only one sentence is present, the function raises an error (Avoids ZeroDivisionError).
if len(sentences) == 1:
Expand All @@ -211,6 +212,11 @@ def summarize(text, ratio=0.2, word_count=None, split=False):

most_important_docs = summarize_corpus(corpus, ratio=ratio if word_count is None else 1)

# If couldn't get important docs, the algorithm ends.
if not most_important_docs:
logger.warning("Couldn't get relevant sentences.")
return [] if split else u""

# Extracts the most important sentences with the selected criterion.
extracted_sentences = _extract_important_sentences(sentences, corpus, most_important_docs, word_count)

Expand Down
99 changes: 40 additions & 59 deletions gensim/test/test_summarization.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,26 +23,24 @@

class TestSummarizationTest(unittest.TestCase):

def test_text_summarization(self):
def _get_text_from_test_data(self, file):
pre_path = os.path.join(os.path.dirname(__file__), 'test_data')
with utils.smart_open(os.path.join(pre_path, file), mode="r") as f:
return f.read()

with utils.smart_open(os.path.join(pre_path, "mihalcea_tarau.txt"), mode="r") as f:
text = f.read()
def test_text_summarization(self):
text = self._get_text_from_test_data("mihalcea_tarau.txt")

# Makes a summary of the text.
generated_summary = summarize(text)

# To be 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 = self._get_text_from_test_data("mihalcea_tarau.summ.txt")

self.assertEqual(generated_summary, summary)

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()
text = self._get_text_from_test_data("mihalcea_tarau.txt")

# Generate the corpus.
sentences = text.split("\n")
Expand All @@ -54,9 +52,8 @@ def test_corpus_summarization(self):
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')
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):
Expand All @@ -67,43 +64,29 @@ def test_corpus_summarization(self):
self.assertTrue(any(all(word in sentence for word in words)) for sentence in summary)

def test_summary_from_unrelated_sentences(self):
# Tests that the summarization of a text with unrelated sentences does not raise an exception.
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()

# Tests that the summarization of a text with unrelated sentences is not empty string.
text = self._get_text_from_test_data("testsummarization_unrelated.txt")
generated_summary = summarize(text)
self.assertNotEqual(generated_summary, u"")

self.assertNotEqual(generated_summary, None)

def test_text_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()
def test_text_summarization_on_short_input_text_is_empty_string(self):
text = self._get_text_from_test_data("testsummarization_unrelated.txt")

# Keeps the first 8 sentences to make the text shorter.
text = "\n".join(text.split('\n')[:8])

self.assertTrue(summarize(text) is not None)

def test_text_summarization_returns_input_on_single_input_sentence(self):
pre_path = os.path.join(os.path.dirname(__file__), 'test_data')
self.assertNotEqual(summarize(text), u"")

with utils.smart_open(os.path.join(pre_path, "testsummarization_unrelated.txt"), mode="r") as f:
text = f.read()
def test_text_summarization_raises_exception_on_single_input_sentence(self):
text = self._get_text_from_test_data("testsummarization_unrelated.txt")

# Keeps the first sentence only.
text = text.split('\n')[0]

self.assertRaises(ValueError, summarize, text)

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()
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]
Expand All @@ -113,19 +96,19 @@ def test_corpus_summarization_raises_exception_on_short_input_text(self):
dictionary = Dictionary(tokens)
corpus = [dictionary.doc2bow(sentence_tokens) for sentence_tokens in tokens]

self.assertTrue(summarize_corpus(corpus) is not None)
self.assertNotEqual(summarize_corpus(corpus), [])

def test_empty_text_summarization_none(self):
self.assertTrue(summarize("") is None)
def test_empty_text_summarization_is_empty_string(self):
self.assertEquals(summarize(""), u"")

def test_empty_corpus_summarization_is_none(self):
self.assertTrue(summarize_corpus([]) is None)
def test_empty_text_summarization_with_split_is_empty_list(self):
self.assertEquals(summarize("", split=True), [])

def test_corpus_summarization_ratio(self):
pre_path = os.path.join(os.path.dirname(__file__), 'test_data')
def test_empty_corpus_summarization_is_empty_list(self):
self.assertEquals(summarize_corpus([]), [])

with utils.smart_open(os.path.join(pre_path, "mihalcea_tarau.txt"), mode="r") as f:
text = f.read()
def test_corpus_summarization_ratio(self):
text = self._get_text_from_test_data("mihalcea_tarau.txt")

# Generate the corpus.
sentences = text.split('\n')
Expand All @@ -142,10 +125,7 @@ def test_corpus_summarization_ratio(self):
self.assertEqual(len(selected_docs), expected_summary_length)

def test_repeated_keywords(self):
pre_path = os.path.join(os.path.dirname(__file__), 'test_data')

with utils.smart_open(os.path.join(pre_path, "testrepeatedkeywords.txt")) as f:
text = f.read()
text = self._get_text_from_test_data("testrepeatedkeywords.txt")

kwds = keywords(text)
self.assertTrue(len(kwds.splitlines()))
Expand All @@ -157,10 +137,7 @@ def test_repeated_keywords(self):
self.assertTrue(len(kwds_lst))

def test_keywords_runs(self):
pre_path = os.path.join(os.path.dirname(__file__), 'test_data')

with utils.smart_open(os.path.join(pre_path, "mihalcea_tarau.txt")) as f:
text = f.read()
text = self._get_text_from_test_data("mihalcea_tarau.txt")

kwds = keywords(text)
self.assertTrue(len(kwds.splitlines()))
Expand All @@ -171,20 +148,24 @@ def test_keywords_runs(self):
kwds_lst = keywords(text, split=True)
self.assertTrue(len(kwds_lst))

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()
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.assertTrue(summarize_corpus(corpus) is None)
self.assertEquals(summarize_corpus(corpus), [])

def test_low_distinct_words_summarization_is_empty_string(self):
text = self._get_text_from_test_data("testlowdistinctwords.txt")
self.assertEquals(summarize(text), u"")

def test_low_distinct_words_summarization_with_split_is_empty_list(self):
text = self._get_text_from_test_data("testlowdistinctwords.txt")
self.assertEquals(summarize(text, split=True), [])

if __name__ == '__main__':
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.DEBUG)
Expand Down