def _set_summarizer():
auto_abstractor = AutoAbstractor()
auto_abstractor.tokenizable_doc = SimpleTokenizer()
auto_abstractor.delimiter_list = [". "] # [".", "\n"]
abstractable_doc = TopNRankAbstractor()
abstractable_doc.set_top_n(10)
return lambda text: auto_abstractor.summarize(text, abstractable_doc)[
"summarize_result"]
def summarize(long_text, num_sentences=NUM_SENTENCE):
# Object of automatic summarization.
auto_abstractor = AutoAbstractor()
# Set tokenizer.
auto_abstractor.tokenizable_doc = SimpleTokenizer()
# Set delimiter for making a list of sentence.
auto_abstractor.delimiter_list = ["?", "!", ".", "\n"]
# Object of abstracting and filtering document.
abstractable_doc = TopNRankAbstractor()
abstractable_doc.set_top_n(num_sentences)
# Summarize document.
result_dict = auto_abstractor.summarize(long_text, abstractable_doc)
# Output result.
res = "".join(result_dict["summarize_result"])
return res
from pysummarization.nlpbase.auto_abstractor import AutoAbstractor
from pysummarization.tokenizabledoc.simple_tokenizer import SimpleTokenizer
from pysummarization.abstractabledoc.top_n_rank_abstractor import TopNRankAbstractor
import sys
document = ""
for line in sys.stdin:
document += line
# Object of automatic summarization.
auto_abstractor = AutoAbstractor()
# Set tokenizer.
auto_abstractor.tokenizable_doc = SimpleTokenizer()
# Set delimiter for making a list of sentence.
auto_abstractor.delimiter_list = [".", "\n\n"]
# Object of abstracting and filtering document.
abstractable_doc = TopNRankAbstractor()
abstractable_doc.set_top_n(5)
# Summarize document.
result_dict = auto_abstractor.summarize(document, abstractable_doc)
# Output result.
for sentence in result_dict["summarize_result"]:
print(sentence.strip())