read_extract.py

import os
import re

import nltk
nltk.download('stopwords')
nltk.download('punkt')


from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.util import ngrams
import spacy
# from gensim.summarization.summarizer import summarize
# from gensim.summarization import keywords

# Abstractive Summarisation
from transformers import BartForConditionalGeneration
from transformers import AutoTokenizer
import torch

# Keyword/Keyphrase Extraction
from keybert import _highlight
from keybert import KeyBERT
from keyphrase_vectorizers import KeyphraseCountVectorizer, KeyphraseTfidfVectorizer
from sklearn.feature_extraction.text import CountVectorizer

import time
import threading
from collections import defaultdict

class AbstractiveSummarizer:

	def __init__(self):
		self.nlp = spacy.load('en_core_web_lg')
		self.summary = ""

	def generate_batch(self, text, tokenizer):
		"""
		Convert the text into multiple sentence parts of appropriate input size to feed to the model

		Arguments:
			text: The License text to summarise
			tokenizer: The tokenizer corresponding to the model used to convert the text into separate words(tokens)

		Returns:
			The text formatted into List of sentences to feed to the model
		"""
		parsed = self.nlp(text)
		sents = [sent.text for sent in parsed.sents]
		max_size = tokenizer.model_max_length

		batch = tokenizer(sents, return_tensors='pt', return_length=True, padding='longest')

		inp_batch = []
		cur_batch = torch.empty((0,), dtype=torch.int64)
		for enc_sent, length in zip(batch['input_ids'], batch['length']):
			cur_size = cur_batch.shape[0]
			if (cur_size + length.item()) <= max_size:
				cur_batch = torch.cat((cur_batch,enc_sent[:length.item()]))
			else:
				inp_batch.append(torch.unsqueeze(cur_batch,0))
				cur_batch = enc_sent[:length.item()]
		inp_batch.append(torch.unsqueeze(cur_batch,0))

		return inp_batch

	def summarize(self, src, tokenizer, model):
		"""
		Function to use the pre-trained model to generate the summary
		Arguments:
			src: License text to summarise
			tokenizer: The tokenizer corresponding to the model used to convert the text into separate words(tokens)
			model: The pre-trained Model object used to perform the summarization

		Returns:
			summary: The summarised texts
		"""
		batch_texts = self.generate_batch(src, tokenizer)

		enc_summary_list = [model.generate(batch, max_length=512) for batch in batch_texts]

		summary_list = [tokenizer.batch_decode(enc_summ, skip_special_tokens=True) for enc_summ in enc_summary_list]
		# orig_list = [tokenizer.batch_decode(batch, skip_special_tokens=True) for batch in batch_texts]

		summary_texts = [summ[0] for summ in summary_list]
		summary = " ".join(summary_texts)
		
		self.summary = summary


	def bart(self, src):
		"""
		Initialize the facebook BART pre-trained model and call necessary functions to summarize
		Arguments:
			src: The text to summarise

		Returns/Set as instance variable:
			The summarized text
		"""

		start_time = time.time()
		model_name = 'facebook/bart-large-cnn'
		device = 'cuda' if torch.cuda.is_available() else 'cpu'
		tokenizer = AutoTokenizer.from_pretrained(model_name)
		model = BartForConditionalGeneration.from_pretrained(model_name).to(device)
		
		self.summarize(src, tokenizer, model)



def get_summary(lic_txt):
	"""
	Summarize the license and return it
	Arguments:
		spdx - Id of License to summarise

	Returns:
		pos_text: The part of the License containing information for permitted use
		neg_text: The part of the License containing information about usage restrictions
		lic_txt: The full license text 
		summary - The generated summary of the license
	"""
	print('Summarising...')
	absSum = AbstractiveSummarizer()

	# Generate summary
	thread = absSum.bart(lic_txt)

	return thread


def extract_ngrams(phrase):
	phrase = re.sub('[^a-zA-Z0-9]',' ', phrase)
	tokens = word_tokenize(phrase)
	res = []
	for num in range(len(tokens)+1):
		temp = ngrams(tokens, num)
		res += [' '.join(grams) for grams in temp]

	return res


def get_highlight_text(text, keywords):
	"""
	Custom function to find exact position of keywords for highlighting
	"""

	text = re.sub('[-/]',' ', text)
	# text = re.sub('(\n *){2,}','\n',text) 
	text = re.sub(' {2,}', ' ', text)

	# Group keywords by length
	kw_len = defaultdict(list)
	for kw in keywords:
		kw_len[len(kw)].append(kw)

	# Use sliding window technique to check equal strings
	spans = []
	for length in kw_len:
		w_start, w_end = 0, length

		while w_end <= len(text):

			for kw in kw_len[length]:
				j = w_start
				eq = True
				for i in range(len(kw)):
					if text[j] != kw[i]:
						eq = False
						break
					j += 1
				if eq:
					spans.append([w_start, w_end])
					break
			
			w_start += 1
			w_end += 1

	if not spans:
		return text

	# merge spans
	spans.sort(key=lambda x: x[0])
	merged = []

	st, end = spans[0][0], spans[0][1]

	for i in range(1, len(spans)):
		s,e = spans[i]

		if st <= s <= end:
			end = max(e, end)
		else:
			merged.append([st, end])
			st, end = s,e
	merged.append([st,end])

	res = []
	sub_start = 0
	for s,e in merged:
		res.append(text[sub_start:s])
		res.append((text[s:e], "", "#f66"))
		sub_start = e
	res.append(text[sub_start:])

	return res



def get_keywords(datatype, task, field, pos_text, neg_text):
	"""
	Summarize the license and generate the good and bad use tags
	Arguments:
		datafield - Type of 'data' used under the license: Eg. Model, Data, Model Derivatives, Source Code
		task - The type of task the model is designed to do
		field - Which 'field' to use the data in: Eg. Medical, Commercial, Non-Commercial, Research
		pos_text: The part of the License containing information for permitted use
		neg_text: The part of the License containing information about usage restrictions

	Returns:
		p_keywords - List of Positive(Permitted use) keywords extracted from summary
		n_keywords - List of Negative(Restriction) keywords extracted from summary
		contrd - boolean flag to show if there is any contradiction or not
		hl_text - the license text formatted to display in a highlighted manner
	"""
	print('Extracting keywords...')

	#[e.lower() for e in list_strings]
	datatype, task, field = datatype.lower(), task.lower(), field.lower()
	#datatype = [e.lower() for e in datatype]
	#task = [e.lower() for e in task]
	#field = [e.lower() for e in field]
	#datatype, task, field = datatype, task, str(field)
	

	stop_words = set(stopwords.words('english'))
	#stops = nltk.corpus.stopwords.words('english')
	#stop_words = set(stops)
	stop_words = stop_words.union({'license', 'licensing', 'licensor', 'copyright', 'copyrights', 'patent'})

	pos_kw_model = KeyBERT()
	neg_kw_model = KeyBERT()

	candidates = []
	for term in [datatype, task, field]:
		candidates += extract_ngrams(term)

	p_kw = pos_kw_model.extract_keywords(docs=pos_text, top_n=40, vectorizer=KeyphraseCountVectorizer(stop_words=stop_words))#, pos_pattern='<N.*>+'))
	n_kw = neg_kw_model.extract_keywords(docs=neg_text, top_n=40, vectorizer=KeyphraseCountVectorizer(stop_words=stop_words))#, pos_pattern='<N.*>+'))

	ngram_max = max([len(word_tokenize(x)) for x in [datatype, task, field]])

	pc_kw = pos_kw_model.extract_keywords(docs=pos_text ,candidates=candidates, keyphrase_ngram_range=(1,ngram_max))
	nc_kw = neg_kw_model.extract_keywords(docs=neg_text ,candidates=candidates, keyphrase_ngram_range=(1,ngram_max))

	# Check contradiction
	all_cont = [kw for (kw,_) in nc_kw]
	cont_terms = set(all_cont).intersection(set(extract_ngrams(field)))
	contrd = True if len(cont_terms) > 0 else False
	hl_text = "" if not contrd else get_highlight_text(neg_text, all_cont)

	p_kw += pc_kw
	n_kw += nc_kw

	p_kw.sort(key=lambda x: x[1], reverse=True)
	n_kw.sort(key=lambda x: x[1], reverse=True)

	p_keywords = [kw for (kw,score) in p_kw if score < 0.5]
	n_keywords = [kw for (kw,score) in n_kw if score < 0.5]

	return p_keywords, n_keywords, contrd, hl_text