【NLP】使用Word Embedding实现中文自动摘要

**使用Word Embedding实现中文自动摘要**

自动摘要是自然语言处理（NLP）的一个重要应用，旨在从原始文本中提取关键信息并生成一个较短的摘要。Word Embedding是一种用于表示单词语义的向量化方法，可以用来改进自动摘要算法。以下是使用Word Embedding实现中文自动摘要的一般流程和代码示例。

**1. 文本预处理**

首先，我们需要对原始文本进行预处理，包括分词、去停词等步骤。

import jiebadef text_preprocessing(text):
 # 分词 words = jieba.cut(text)
 # 去停词 stop_words = set(["", "", ""]) # 停用词列表 filtered_words = [word for word in words if word not in stop_words]
 return filtered_words

import numpy as npdef load_glove_model(glove_file):
 glove_dict = {}
 with open(glove_file, 'r') as f:
 for line in f:
 values = line.split()
 word = values[0]
 vector = np.asarray([float(val) for val in values[1:]], dtype='float32')
 glove_dict[word] = vector return glove_dictdef get_word_embedding(words, glove_model):
 embeddings = []
 for word in words:
 if word in glove_model:
 embeddings.append(glove_model[word])
 else:
 # 使用平均值作为缺失值 avg_embedding = np.mean([glove_model[w] for w in glove_model if w != ""], axis=0)
 embeddings.append(avg_embedding)
 return np.array(embeddings)

from sklearn.feature_extraction.text import TfidfVectorizerfrom sklearn.cluster import KMeansdef get_tfidf_matrix(words):
 vectorizer = TfidfVectorizer()
 tfidf_matrix = vectorizer.fit_transform([' '.join(words)])
 return tfidf_matrix.toarray()

def get_summary(tfidf_matrix, kmeans_model):
 clusters = kmeans_model.predict(tfidf_matrix)
 summary_words = []
 for i in range(kmeans_model.n_clusters):
 cluster_words = [words[j] for j in range(len(words)) if clusters[j] == i]
 summary_words.extend(cluster_words[:3]) # 取前三个词作为摘要 return summary_words# 加载GloVe模型glove_file = "glove.6B.100d.txt"
glove_model = load_glove_model(glove_file)

# 加载原始文本text = "这是一个测试文本..."

# 文本预处理words = text_preprocessing(text)

# 获取Word Embeddingembeddings = get_word_embedding(words, glove_model)

# 获取TF-IDF矩阵tfidf_matrix = get_tfidf_matrix(words)

# K-Means聚类kmeans_model = KMeans(n_clusters=5)
clusters = kmeans_model.fit_predict(tfidf_matrix)

# 获取摘要summary_words = get_summary(tfidf_matrix, kmeans_model)

print(summary_words) # 输出摘要