IndexError: список индексов выходит за пределы диапазона [sumrize_text.append ("" .join (ranked_sentence [i] [1]))] - PullRequest
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IndexError: список индексов выходит за пределы диапазона [sumrize_text.append ("" .join (ranked_sentence [i] [1]))]

0 голосов
/ 23 апреля 2019

Я написал некоторый код на Python для генерации краткой информации о текстовом файле.Я получаю ошибку IndexError: list index of range для этой строки моего кода; 

   for i in range(top_n):
      summarize_text.append(" ".join(ranked_sentence[i][1]))

Я следовал руководству по реализации процесса.https://towardsdatascience.com/understand-text-summarization-and-create-your-own-summarizer-in-python-b26a9f09fc70 Я не нашел помощи по ее обзорам или комментариям.Я попытался найти подобные проблемы здесь безрезультатно.

Мой полный код; 

from nltk.corpus import stopwords
from nltk.cluster.util import cosine_distance
import numpy as np
import networkx as nx

def read_article(file_name):
    file = open(file_name, "r+",encoding="utf-8")
    filedata = file.readlines()
    article = filedata[0].split(". ")
    sentences = []

    for sentence in article:
        print(sentence)
        sentences.append(sentence.replace("[^a-zA-Z]", " ").split(" "))
    sentences.pop() 

    return sentences

def sentence_similarity(sent1, sent2, stopwords=None):
    if stopwords is None:
        stopwords = []

    sent1 = [w.lower() for w in sent1]
    sent2 = [w.lower() for w in sent2]

    all_words = list(set(sent1 + sent2))

    vector1 = [0] * len(all_words)
    vector2 = [0] * len(all_words)

    # build the vector for the first sentence
    for w in sent1:
        if w in stopwords:
            continue
        vector1[all_words.index(w)] += 1

    # build the vector for the second sentence
    for w in sent2:
        if w in stopwords:
            continue
        vector2[all_words.index(w)] += 1

    return 1 - cosine_distance(vector1, vector2)

def build_similarity_matrix(sentences, stop_words):
    # Create an empty similarity matrix
    similarity_matrix = np.zeros((len(sentences), len(sentences)))

    for idx1 in range(len(sentences)):
        for idx2 in range(len(sentences)):
            if idx1 == idx2: #ignore if both are same sentences
                continue 
            similarity_matrix[idx1][idx2] = sentence_similarity(sentences[idx1], sentences[idx2], stop_words)

    return similarity_matrix


def generate_summary(file_name, top_n=5):
    stop_words = stopwords.words('english')
    summarize_text = []

    # Step 1 - Read text anc split it
    sentences =  read_article(file_name)

    # Step 2 - Generate Similary Martix across sentences
    sentence_similarity_martix = build_similarity_matrix(sentences, stop_words)

    # Step 3 - Rank sentences in similarity martix
    sentence_similarity_graph = nx.from_numpy_array(sentence_similarity_martix)
    scores = nx.pagerank(sentence_similarity_graph)

    # Step 4 - Sort the rank and pick top sentences
    ranked_sentence = sorted(((scores[i],s) for i,s in enumerate(sentences)), reverse=True)    
    print("Indexes of top ranked_sentence order are ", ranked_sentence)    

 # **THE ERROR**   
 for i in range(top_n):
      summarize_text.append(" ".join(ranked_sentence[i][1]))

    # Step 5 - Offcourse, output the summarize texr
    print("Summarize Text: \n", ". ".join(summarize_text))

# let's begin
generate_summary( "F:\\Girivraaj\\tmp\\document8.txt", 2)

Ошибка показывается для; 

 for i in range(top_n):
      summarize_text.append(" ".join(ranked_sentence[i][1]))

(выделено жирным шрифтом)в полном коде)

Ожидаемый результат будет сводным.

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