spacysum.py

from textblob import TextBlob 
import spacy
from gensim.summarization import summarize
import en_core_web_sm
from spacy.lang.pt.stop_words import STOP_WORDS
from sklearn.feature_extraction.text import CountVectorizer

def summarization_spacy(text):

        
    nlp = en_core_web_sm.load()
    
    
    doc = nlp(text)


    corpus = [sent.text.lower() for sent in doc.sents ]
    
    cv = CountVectorizer(stop_words=list(STOP_WORDS))   
    cv_fit=cv.fit_transform(corpus)    
    word_list = cv.get_feature_names();    
    count_list = cv_fit.toarray().sum(axis=0)    

    
    word_frequency = dict(zip(word_list,count_list))

    val=sorted(word_frequency.values())

    # Check words with higher frequencies
    higher_word_frequencies = [word for word,freq in word_frequency.items() if freq in val[-3:]]
    # print("\nWords with higher frequencies: ", higher_word_frequencies)

    # gets relative frequencies of words
    higher_frequency = val[-1]
    for word in word_frequency.keys():  
        word_frequency[word] = (word_frequency[word]/higher_frequency)


    # SENTENCE RANKING: the rank of sentences is based on the word frequencies
    sentence_rank={}
    for sent in doc.sents:
        for word in sent :       
            if word.text.lower() in word_frequency.keys():            
                if sent in sentence_rank.keys():
                    sentence_rank[sent]+=word_frequency[word.text.lower()]
                else:
                    sentence_rank[sent]=word_frequency[word.text.lower()]
            else:
                continue

    top_sentences=(sorted(sentence_rank.values())[::-1])
    top_sent=top_sentences[:3]

    # Mount summary
    summary=[]
    for sent,strength in sentence_rank.items():  
        if strength in top_sent:
            summary.append(sent)

    summary = str(summary[0])+str(summary[1])+str(summary[2])
    # return orinal text and summary
    return summary