T&C Summarization Model
T&C Summarization Model based on sshleifer/distilbart-cnn-6-6,
This abstractive summarization model is a part of a bigger end-to-end T&C summarizer pipeline which is preceded by LSA (Latent Semantic Analysis) extractive summarization. The extractive summarization shortens the T&C to be further summarized by this model.
Finetuning Corpus
We collaborated with TOSDR to work with their data, and the model is finetuned accordingly. The article and summarization text is reduced via extractive summarization before it is finetuned to the model.
Contact Us
This abstractive model finetuning is the continuation of the Christmas Project 2021 done in ML6: https://bit.ly/XmasProjects .
Load Finetuned Model
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("ml6team/distilbart-tos-summarizer-tosdr")
model = AutoModelForSeq2SeqLM.from_pretrained("ml6team/distilbart-tos-summarizer-tosdr")
Code Sample
This sample requires sumy, the LSA Extractive Summarization library, as additional package to run.
import re
import nltk
nltk.download('punkt')
from sumy.parsers.plaintext import PlaintextParser
from sumy.nlp.tokenizers import Tokenizer
from sumy.nlp.stemmers import Stemmer
from sumy.summarizers.lsa import LsaSummarizer
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
LANGUAGE = "english"
EXTRACTED_ARTICLE_SENTENCES_LEN = 12
stemmer = Stemmer(LANGUAGE)
lsa_summarizer = LsaSummarizer(stemmer)
tokenizer = AutoTokenizer.from_pretrained("ml6team/distilbart-tos-summarizer-tosdr")
model = AutoModelForSeq2SeqLM.from_pretrained("ml6team/distilbart-tos-summarizer-tosdr")
def get_extractive_summary(text, sentences_count):
parser = PlaintextParser.from_string(text, Tokenizer(LANGUAGE))
summarized_info = lsa_summarizer(parser.document, sentences_count)
summarized_info = [element._text for element in summarized_info]
return ' '.join(summarized_info)
def get_summary(dict_summarizer_model, dict_tokenizer, text_content):
text_content = get_extractive_summary(text_content, EXTRACTED_ARTICLE_SENTENCES_LEN)
tokenizer = dict_tokenizer['tokenizer']
model = dict_summarizer_model['model']
inputs = tokenizer(text_content, max_length=dict_tokenizer['max_length'], truncation=True, return_tensors="pt")
outputs = model.generate(
inputs["input_ids"], max_length=dict_summarizer_model['max_length'], min_length=dict_summarizer_model['min_length'],
)
summarized_text = tokenizer.decode(outputs[0])
match = re.search(r"<s>(.*)</s>", summarized_text)
if match is not None: summarized_text = match.group(1)
return summarized_text.replace('<s>', '').replace('</s>', '')
test_tos = """
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"""
model_dict = {
'model': model,
'max_length': 512,
'min_length': 4
}
tokenizer_dict = {
'tokenizer': tokenizer,
'max_length': 1024
}
print(get_summary(model_dict, tokenizer_dict, test_tos))