def _set_graph_edge_weights(graph):
"""Sets weights using BM25 algorithm. Leaves small weights as zeroes. If all weights are fairly small,
forces all weights to 1, inplace.
Parameters
----------
graph : :class:`~gensim.summarization.graph.Graph`
Given graph.
"""
documents = graph.nodes()
weights = _bm25_weights(documents)
for i in xrange(len(documents)):
for j in xrange(len(documents)):
if i == j or weights[i][j] < WEIGHT_THRESHOLD:
continue
sentence_1 = documents[i]
sentence_2 = documents[j]
edge_1 = (sentence_1, sentence_2)
edge_2 = (sentence_2, sentence_1)
if not graph.has_edge(edge_1):
graph.add_edge(edge_1, weights[i][j])
if not graph.has_edge(edge_2):
graph.add_edge(edge_2, weights[j][i])
# Handles the case in which all similarities are zero.
# The resultant summary will consist of random sentences.
if all(graph.edge_weight(edge) == 0 for edge in graph.edges()):
_create_valid_graph(graph)
def _set_graph_edge_weights(graph):
"""Sets weights using BM25 algorithm. Leaves small weights as zeroes. If all weights are fairly small,
forces all weights to 1, inplace.
Parameters
----------
graph : :class:`~gensim.summarization.graph.Graph`
Given graph.
"""
documents = graph.nodes()
weights = _bm25_weights(documents)
for i, doc_bow in enumerate(weights):
if i % 1000 == 0 and i > 0:
logger.info('PROGRESS: processing %s/%s doc (%s non zero elements)', i, len(documents), len(doc_bow))
for j, weight in doc_bow:
if i == j or weight < WEIGHT_THRESHOLD:
continue
edge = (documents[i], documents[j])
if not graph.has_edge(edge):
graph.add_edge(edge, weight)
# Handles the case in which all similarities are zero.
# The resultant summary will consist of random sentences.
if all(graph.edge_weight(edge) == 0 for edge in graph.iter_edges()):
_create_valid_graph(graph)
def _set_graph_edge_weights(graph):
"""Sets weights using BM25 algorithm. Leaves small weights as zeroes. If all weights are fairly small,
forces all weights to 1, inplace.
Parameters
----------
graph : :class:`~gensim.summarization.graph.Graph`
Given graph.
"""
documents = graph.nodes()
weights = _bm25_weights(documents)
for i in xrange(len(documents)):
for j in xrange(len(documents)):
if i == j or weights[i][j] < WEIGHT_THRESHOLD:
continue
sentence_1 = documents[i]
sentence_2 = documents[j]
edge_1 = (sentence_1, sentence_2)
edge_2 = (sentence_2, sentence_1)
if not graph.has_edge(edge_1):
graph.add_edge(edge_1, weights[i][j])
if not graph.has_edge(edge_2):
graph.add_edge(edge_2, weights[j][i])
# Handles the case in which all similarities are zero.
# The resultant summary will consist of random sentences.
if all(graph.edge_weight(edge) == 0 for edge in graph.edges()):
_create_valid_graph(graph)
def _set_graph_edge_weights(graph):
"""Sets weights using BM25 algorithm. Leaves small weights as zeroes. If all weights are fairly small,
forces all weights to 1, inplace.
Parameters
----------
graph : :class:`~gensim.summarization.graph.Graph`
Given graph.
"""
documents = graph.nodes()
weights = _bm25_weights(documents)
for i, doc_bow in enumerate(weights):
if i % 1000 == 0 and i > 0:
logger.info(
'PROGRESS: processing %s/%s doc (%s non zero elements)', i,
len(documents), len(doc_bow))
for j, weight in doc_bow:
if i == j or weight < WEIGHT_THRESHOLD:
continue
edge = (documents[i], documents[j])
if not graph.has_edge(edge):
graph.add_edge(edge, weight)
# Handles the case in which all similarities are zero.
# The resultant summary will consist of random sentences.
if all(graph.edge_weight(edge) == 0 for edge in graph.iter_edges()):
_create_valid_graph(graph)
def _set_graph_edge_weights(graph):
documents = graph.nodes()
weights = _bm25_weights(documents)
for i in xrange(len(documents)):
for j in xrange(len(documents)):
if i == j or weights[i][j] < WEIGHT_THRESHOLD:
continue
sentence_1 = documents[i]
sentence_2 = documents[j]
edge_1 = (sentence_1, sentence_2)
edge_2 = (sentence_2, sentence_1)
if not graph.has_edge(edge_1):
graph.add_edge(edge_1, weights[i][j])
if not graph.has_edge(edge_2):
graph.add_edge(edge_2, weights[j][i])
# Handles the case in which all similarities are zero.
# The resultant summary will consist of random sentences.
if all(graph.edge_weight(edge) == 0 for edge in graph.edges()):
_create_valid_graph(graph)
def _set_graph_edge_weights(graph):
documents = graph.nodes()
weights = _bm25_weights(documents)
for i in xrange(len(documents)):
for j in xrange(len(documents)):
if i == j:
continue
sentence_1 = documents[i]
sentence_2 = documents[j]
edge_1 = (sentence_1, sentence_2)
edge_2 = (sentence_2, sentence_1)
if not graph.has_edge(edge_1):
graph.add_edge(edge_1, weights[i][j])
if not graph.has_edge(edge_2):
graph.add_edge(edge_2, weights[j][i])
# Handles the case in which all similarities are zero.
# The resultant summary will consist of random sentences.
if all(graph.edge_weight(edge) == 0 for edge in graph.edges()):
_create_valid_graph(graph)
