使用文本/空间的NLP摘要化

我想从这篇课文中用一句话总结一下。我正在使用textacy.py。 这是我的密码：

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

我试过了

textacy.extract.words
textacy.extract.entities
textacy.extract.ngrams
textacy.extract.noun_chunks
textacy.ke.textrank

一切都按照书上说的进行，但结果并不完美。 我想要的是类似于“Kasiapa娶了Kadru和Vinata姐妹”或“Kasiapa给了Kadru和Vinata兄弟”。

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你能告诉我怎么做吗？或者建议我使用一些替代软件包？

只是一个更新。我已经能够对“Sauti”的句子进行页面排序。以下是按pagerank降序排列的结果：

(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', '.'])]   

结果不是我想要的，但令人印象深刻。 我使用了以下库：

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

只是想和大家分享一下

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谢谢

我尝试了spacy+textrank，来自huggingface的bart对这些数据进行了总结，但是没有人返回你想要的句子。另外，看看语料库，不太清楚为什么你选择的句子会是摘要。@Bitswazsky谢谢你的评论。实际上，我是在模仿《拥抱的脸》，我是在总结一本很长的书。如果这样做有效的话，我的问题会解决很多。谢谢你的努力。我采取了另一种方法。我将离开TEXTACY，采用具有PageRank的NLTK。我可以使用PageRank来进行TextRank。