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import util
import abstract
import classification
import inference
import outline
from inference import BertClassificationModel
# input:file/text,topic_num,max_length,output_choice
# output:file/text/topic_sentence
# file_process:
# in util
# read file code
# file to json_text
# convert:
# in util
# convert code
# json_text to text
# process:
# in util
# text process code
# del stop seg
def texClear(article):
sentencesCleared = [util.clean_text(sentence) for sentence in article]
return sentencesCleared
def textToAb(sentences, article, topic_num, max_length):
central_sentences = abstract.abstruct_main(sentences, topic_num)
groups = classification.classify_by_topic(article, central_sentences)
groups = util.article_to_group(groups, central_sentences)
title_dict,title = util.generation(groups, max_length)
# ans:
# {Ai_abstruct:(main_sentence,paragraph)}
matrix = inference.inference_matrix(title)
_,outline_list = outline.passage_outline(matrix,title)
output = util.formate_text(title_dict,outline_list)
keys = []
for key in title.keys():
keys.append(key)
return keys, output |