NLP summerization using textacy/spacy - nlp

Question

I want to generate a summary maybe in one sentence from this text. I am using textacy.py.
Here is my code:
import textacy
import textacy.keyterms
import textacy.extract
import spacy
nlp = spacy.load('en_core_web_sm')
text = '''Sauti said, 'O thou that art blest with longevity, I shall narrate the history of Astika as I heard it from my father.
O Brahmana, in the golden age, Prajapati had two daughters.
O sinless one, the sisters were endowed with wonderful beauty.
Named Kadru and Vinata, they became the wives of Kasyapa.
Kasyapa derived great pleasure from his two wedded wives and being gratified he, resembling Prajapati himself, offered to give each of them a boon.
Hearing that their lord was willing to confer on them their choice blessings, those excellent ladies felt transports of joy.
Kadru wished to have for sons a thousand snakes all of equal splendour.
And Vinata wished to bring forth two sons surpassing the thousand offsprings of Kadru in strength, energy, size of body, and prowess.
Unto Kadru her lord gave that boon about a multitude of offspring.
And unto Vinata also, Kasyapa said, 'Be it so!' Then Vinata, having; obtained her prayer, rejoiced greatly.
Obtaining two sons of superior prowess, she regarded her boon fulfilled.
Kadru also obtained her thousand sons of equal splendour.
'Bear the embryos carefully,' said Kasyapa, and then he went into the forest, leaving his two wives pleased with his blessings.'''
doc = textacy.make_spacy_doc(text, 'en_core_web_sm')
sentobj = nlp(text)
sentences = textacy.extract.subject_verb_object_triples(sentobj)
summary=''
for i, x in enumerate(sentences):
subject, verb, fact = x
print('Fact ' + str(i+1) + ': ' + str(subject) + ' : ' + str(verb) + ' : ' + str(fact))
summary += 'Fact ' + str(i+1) + ': ' + (str(fact))
Results are as follows:
Fact 1: I : shall narrate : history
Fact 2: I : heard : it
Fact 3: they : became : wives
Fact 4: Kasyapa : derived : pleasure
Fact 5: ladies : felt : transports
Fact 6: Kadru : wished : have
Fact 7: Vinata : wished : to bring
Fact 8: lord : gave : boon
Fact 9: Kasyapa : said : Be
Fact 10: Vinata : obtained : prayer
Fact 11: she : regarded : boon
Fact 12: Kadru : obtained : sons
I tried
textacy.extract.words
textacy.extract.entities
textacy.extract.ngrams
textacy.extract.noun_chunks
textacy.ke.textrank
Everything is working as per the book but results are not perfect.
I am wanting something like "Kasyapa married Kadru and Vinata sisters" or "Kasyapa gave embroys to Kadru and Vinata".
Can you please suggest me how to do this? Or suggest me some alternative packages to use?

Answer 1

Just an update. I have been able to pagerank the "Sauti" sentences. Here are the results in descending order of pagerank:
(0.0869526908422304, ['O', 'Brahmana', ',', 'in', 'the', 'golden', 'age', ',', 'Prajapati', 'had', 'two', 'daughters', '.']),
(0.08675152795526771, ['Named', 'Kadru', 'and', 'Vinata', ',', 'they', 'became', 'the', 'wives', 'of', 'Kasyapa', '.']),
(0.08607926397402169, ['And', 'Vinata', 'wished', 'to', 'bring', 'forth', 'two', 'sons', 'surpassing', 'the', 'thousand', 'offsprings', 'of', 'Kadru', 'in', 'strength', ',', 'energy', ',', 'size', 'of', 'body', ',', 'and', 'prowess', '.']),
(0.08096858541855065, ['Kasyapa', 'derived', 'great', 'pleasure', 'from', 'his', 'two', 'wedded', 'wives', 'and', 'being', 'gratified', 'he', ',', 'resembling', 'Prajapati', 'himself', ',', 'offered', 'to', 'give', 'each', 'of', 'them', 'a', 'boon', '.']),
(0.08025844559654187, ['And', 'unto', 'Vinata', 'also', ',', 'Kasyapa', 'said', ',', '("\'Be",', "'VBD", 'it', 'so', '!', '("\'",', '"\'\'"),', 'Then', 'Vinata', ',', 'having', ';', 'obtained', 'her', 'prayer', ',', 'rejoiced', 'greatly', '.']),
(0.07764697882919071, ['Obtaining', 'two', 'sons', 'of', 'superior', 'prowess', ',', 'she', 'regarded', 'her', 'boon', 'fulfilled', '.']),
(0.07717129674341844, ['("\'Bear",', "'IN", 'the', 'embryos', 'carefully', ',', '("\'",', '"\'\'"),', 'said', 'Kasyapa', ',', 'and', 'then', 'he', 'went', 'into', 'the', 'forest', ',', 'leaving', 'his', 'two', 'wives', 'pleased', 'with', 'his', 'blessings', '.']),
(0.0768816552210493, ['Kadru', 'also', 'obtained', 'her', 'thousand', 'sons', 'of', 'equal', 'splendour', '.']),
(0.07172005226142254, ['Kadru', 'wished', 'to', 'have', 'for', 'sons', 'a', 'thousand', 'snakes', 'all', 'of', 'equal', 'splendour', '.']),
(0.06953411123175395, ['Unto', 'Kadru', 'her', 'lord', 'gave', 'that', 'boon', 'about', 'a', 'multitude', 'of', 'offspring', '.']),
(0.06943939082844, ['Sauti\\', 'said', ',', '("\'",', '"\'\'"),', 'O', 'thou', 'that', 'art', 'blest', 'with', 'longevity', ',', 'I', 'shall', 'narrate', 'the', 'history', 'of', 'Astika', 'as', 'I', 'heard', 'it', 'from', 'my', 'father', '.']),
(0.06888390365265022, ['O', 'sinless', 'one', ',', 'the', 'sisters', 'were', 'endowed', 'with', 'wonderful', 'beauty', '.']),
(0.0677120974454628, ['Hearing', 'that', 'their', 'lord', 'was', 'willing', 'to', 'confer', 'on', 'them', 'their', 'choice', 'blessings', ',', 'those', 'excellent', 'ladies', 'felt', 'transports', 'of', 'joy', '.'])]
Results are not what I was looking for but are impressive.
I used these following libraries:
import nltk.tokenize as tk
from nltk import sent_tokenize, word_tokenize
from nltk.cluster.util import cosine_distance
from nltk.corpus import brown, stopwords
import networkx as nx
Just wanted to share this with you all.
thanks