python - Python Text Summarizer - 维护句子顺序

Question

我正在自学 python 并完成了一个基本的文本摘要器。我对摘要文本几乎感到满意，但想进一步完善最终产品。

该代码正确执行了一些标准文本处理（标记化、删除停用词等）。然后代码根据加权词频对每个句子进行评分。我正在使用 heapq.nlargest() 方法返回前 7 个句子，根据我的示例文本，我觉得这些句子做得很好。

我面临的问题是前 7 个句子是从最高分 -> 最低分返回的。我明白为什么会这样。我宁愿保持与原文中相同的句子顺序。我已经包含了相关的代码，希望有人可以指导我解决问题。

#remove all stopwords from text, build clean list of lower case words
clean_data = []
for word in tokens:
    if str(word).lower() not in stoplist:
        clean_data.append(word.lower())

#build dictionary of all words with frequency counts: {key:value = word:count}
word_frequencies = {}
for word in clean_data:
    if word not in word_frequencies.keys():
        word_frequencies[word] = 1
    else:
        word_frequencies[word] += 1

#print(word_frequencies.items())

#update the dictionary with a weighted frequency 
maximum_frequency = max(word_frequencies.values())
#print(maximum_frequency)

for word in word_frequencies.keys():
    word_frequencies[word] = (word_frequencies[word]/maximum_frequency)

#print(word_frequencies.items())

#iterate through each sentence and combine the weighted score of the underlying word
sentence_scores = {}

for sent in sentence_list:
    for word in nltk.word_tokenize(sent.lower()):
        if word in word_frequencies.keys():
            if len(sent.split(' ')) < 30:
                if sent not in sentence_scores.keys():
                    sentence_scores[sent] = word_frequencies[word]
                else:
                    sentence_scores[sent] += word_frequencies[word]

#print(sentence_scores.items())                    

summary_sentences = heapq.nlargest(7, sentence_scores, key = sentence_scores.get)

summary = ' '.join(summary_sentences)    

print(summary)

我正在使用以下文章进行测试：https ://www.bbc.com/news/world-australia-45674716

失去一切的银行客户 他还批评了监管机构对银行和金融公司的不当行为。它还收到了超过 9,300 份来自银行、财务顾问、养老基金和保险公司的不当行为指控。”

作为期望输出的示例：上面的第三句话，“今年的皇家委员会，该国最高的公共调查形式，揭露了该行业普遍存在的不当行为。” 实际上出现在原始文章中的“澳大利亚银行查询：他们不在乎他们伤害了谁”之前，我希望输出保持该句子顺序。

标签： pythonnlp

Answer 1

得到它的工作，离开这里以防其他人好奇：

#iterate through each sentence and combine the weighted score of the underlying word
sentence_scores = {}
cnt = 0
for sent in sentence_list:
    sentence_scores[sent] = []
    score = 0
    for word in nltk.word_tokenize(sent.lower()):
        if word in word_frequencies.keys():
            if len(sent.split(' ')) < 30:
                if sent not in sentence_scores.keys():
                    score = word_frequencies[word]
                else:
                    score += word_frequencies[word]
    sentence_scores[sent].append(score)
    sentence_scores[sent].append(cnt)
    cnt = cnt + 1


#Sort the dictionary using the score in descending order and then index in ascending order
#Getting the top 7 sentences
#Putting them in 1 string variable
from operator import itemgetter
top7 = dict(sorted(sentence_scores.items(), key=itemgetter(1), reverse = True)[0:7])
#print(top7)
def Sort(sub_li):
    return(sorted(sub_li, key = lambda sub_li: sub_li[1])) 
sentence_summary = Sort(top7.values())
summary = ""
for value in sentence_summary:
    for key in top7:
        if top7[key] == value:
            summary = summary + key
print(summary)