#!/usr/bin/env python
# encoding: utf-8
import sys
import textrank
## Stage 1:
## * perform statistical parsing/tagging on a document in JSON format
##
## INPUTS: <stage0>
## OUTPUT: JSON format `ParsedGraf(id, sha1, graf)`
if __name__ == "__main__":
path = sys.argv[1]
for graf in textrank.parse_doc(textrank.json_iter(path)):
print(textrank.pretty_print(graf._asdict()))
#!/usr/bin/env python
# encoding: utf-8
import sys
import textrank
DEBUG = False # True
## Stage 2:
## * collect and normalize the key phrases from a parsed document
##
## INPUTS: <stage1>
## OUTPUT: JSON format `RankedLexeme(text, rank, ids, pos)`
if __name__ == "__main__":
path = sys.argv[1]
graph, ranks = textrank.text_rank(path)
textrank.render_ranks(graph, ranks)
# output as JSON
for rl in textrank.normalize_key_phrases(path, ranks):
print(textrank.pretty_print(rl._asdict()))
#!/usr/bin/env python
# encoding: utf-8
import sys
import textrank
DEBUG = True # False
## "It scrubs its unreadable characters out of its text stream...
## Then it generates a JSON doc again."
if __name__ == "__main__":
path = sys.argv[1]
lines = []
with open(path, 'r') as f:
for line in f.readlines():
line = line.strip().replace('“', '"').replace('”', '"')
line = line.replace('…', '...').replace('–', '-')
line = line.replace("’", "'").replace("`", "'")
lines.append(line)
j = {}
j["id"] = "777"
j["text"] = " ".join(lines)
print(textrank.pretty_print(j))
#!/usr/bin/env python
# encoding: utf-8
import sys
import textrank
## Stage 1:
## * perform statistical parsing/tagging on a document in JSON format (Stage 0)
## * output is in JSON format
if __name__ == "__main__":
path = sys.argv[1]
for graf in textrank.parse_doc(textrank.json_iter(path)):
print(textrank.pretty_print(graf))
#!/usr/bin/env python
# encoding: utf-8
import sys
import textrank
DEBUG = False # True
## Stage 2:
## * summarize key phrases from a parsed document in JSON format (Stage 1)
## * output is in JSON format
if __name__ == "__main__":
path = sys.argv[1]
graph, ranks, summary = textrank.text_rank(path)
if DEBUG:
textrank.render_ranks(graph, ranks)
for p in textrank.normalize_keyphrases(summary):
print("%0.4f\t%s" % (p.rank, p.text), p.ids)
# output as JSON
for norm in textrank.get_kernel(summary):
print(textrank.pretty_print(norm._asdict()))
