我正在使用Nltk從句子中刪除停用詞。例如。"I would love to fly again via American Airlines"結果："Love to fly American Airlines"我曾嘗試過以下代碼：# Tokenizing the text txt = "I love to fly with American Airlines"stopWords = set(stopwords.words("english")) words = word_tokenize(txt) # Creating a frequency table to keep the  # score of each word freqTable = dict() for word in words:     word = word.lower()     if word in stopWords:         continue    if word in freqTable:         freqTable[word] += 1    else:         freqTable[word] = 1# Creating a dictionary to keep the score # of each sentence sentences = sent_tokenize(txt) sentenceValue = dict() for sentence in sentences:     for word, freq in freqTable.items():         if word in sentence.lower():             if sentence in sentenceValue:                 sentenceValue[sentence] += freq             else:                 sentenceValue[sentence] = freq sumValues = 0for sentence in sentenceValue:     sumValues += sentenceValue[sentence] # Average value of a sentence from the original text average = int(sumValues / len(sentenceValue)) # Storing sentences into our summary. summary = '' for sentence in sentences:     if (sentence in sentenceValue) and (sentenceValue[sentence] > (1.2 * average)):         summary += " " + sentence print("Summary: " + summary)這個結果是一個空字符串，因為我認為這個句子太短而無法Nltk工作。只是研究是否有更簡單的方法，我打算為此訓練一個模型。