├── TextRank ├── __init__.py ├── const.py ├── textPro.py ├── textRank.py ├── utils.py └── statics │ └── stopwords.txt └── README.md /TextRank/__init__.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # _*_coding:utf-8 _*_ 3 | # @Time    :2020/4/4 19:14 4 | # @Author  :Abner Wong 5 | # @Software: PyCharm -------------------------------------------------------------------------------- /TextRank/const.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # _*_coding:utf-8 _*_ 3 | # @Time    :2020/4/2 18:55 4 | # @Author  :Abner Wong 5 | # @Software: PyCharm 6 | import os 7 | 8 | module_path = os.path.dirname(__file__) 9 | 10 | STOPWORDS = list() 11 | with open(module_path+'/statics/stopwords.txt', 'r', encoding='utf-8') as f: 12 | for w in f.readlines(): 13 | STOPWORDS.append(w.strip()) 14 | 15 | PROPERTY_FILTER = ['an', 'i', 'j', 'l', 'n', 'nr', 'nrfg', 'ns', 'nt', 'nz', 't', 'v', 'vd', 'vn', 'eng'] 16 | 17 | 18 | 19 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # textrank 2 | 基于TextRank算法实现中文文本的关键词提取和摘要任务，核心计算代码保持与论文一致。 3 | 4 | ## 原理 5 | 6 | TextRank的论文： 7 | 8 | > Mihalcea R, Tarau P. TextRank: Bringing order into texts[C]. Association for Computational Linguistics, 2004. 9 | ___ 10 | ## 依赖 11 | +jieba >= 0.35 12 | +numpy >= 1.7.1 13 | 14 | ___ 15 | ## 示例 16 | 17 | ```python 18 | from TextRank import textRank 19 | 20 | text = """欧亚经济委员会执委会一体化与宏观经济委员格拉济耶夫日前接受新华社记者采访时高度评价中国抗击新冠疫情工作，\ 21 | 并表示期待欧亚经济联盟与中国加强抗疫合作，共同推动地区发展。格拉济耶夫说，中国依靠治理体系与全国人民协同努力，\ 22 | 在抗疫工作上取得极大成效。中国采取的措施符合全球利益。格拉济耶夫认为，中国经济将会快速恢复，欧亚经济联盟许多企业与中国市场联系紧密，\ 23 | 应与中国加强合作，采取协调措施降低此次疫情带来的消极影响。格拉济耶夫建议，面对疫情，欧亚经济联盟与中国扩大信息技术应用，\ 24 | 推进商品清关程序自动化，更广泛地利用相关机制，为对外经济活动参与者建立绿色通道。谈及双方在医学卫生领域的合作时，\ 25 | 格拉济耶夫说：“我们应从当前考验中汲取经验，在生物安全领域制定共同规划并联合开展生物工程研究。”格拉济耶夫还表示，\ 26 | 俄罗斯与其他欧亚经济联盟国家金融市场更易受国际投机行为影响。欧亚经济联盟应借鉴中国的人民币国际化经验，加强与中国银行体系和金融市场对接。\ 27 | 欧亚经济联盟成立于2015年，成员国包括俄罗斯、哈萨克斯坦、白俄罗斯、吉尔吉斯斯坦和亚美尼亚。欧亚经济委员会执委会是欧亚经济联盟最高权力机构。""" 28 | 29 | T = textRank.TextRank(text,pr_config={'alpha': 0.85, 'max_iter': 100}) 30 | 31 | # 提取前10个关键词 32 | T.get_n_keywords(10) 33 | 34 | # 提取前3个句子作为摘要 35 | 36 | ``` 37 | 38 | ## 输出 39 | 40 | ```plain 41 | # 关键词 42 | [('中国', 0.0409732016371885), 43 | ('欧亚', 0.020288574056379977), 44 | ('联盟', 0.020095514492593516), 45 | ('疫情', 0.01896670992106251), 46 | ('合作', 0.01762300199967477), 47 | ('经济', 0.017491198051334592), 48 | ('加强', 0.014129557788440673), 49 | ('金融市场', 0.013893142456055885), 50 | ('体系', 0.012966637917644607), 51 | ('俄罗斯', 0.012933808546504099)] 52 | 53 | # 句子 54 | 55 | [('欧亚经济委员会执委会一体化与宏观经济委员格拉济耶夫日前接受新华社记者采访时高度评价中国抗击新冠疫情工作，并表示期待欧亚经济联盟与中国加强抗疫合作，共同推动地区发展', 56 | 0.14281076822079067), 57 | ('格拉济耶夫认为，中国经济将会快速恢复，欧亚经济联盟许多企业与中国市场联系紧密，应与中国加强合作，采取协调措施降低此次疫情带来的消极影响', 58 | 0.12857514563980263), 59 | ('欧亚经济联盟应借鉴中国的人民币国际化经验，加强与中国银行体系和金融市场对接', 0.11960701215088403)] 60 | 61 | ``` 62 | -------------------------------------------------------------------------------- /TextRank/textPro.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # _*_coding:utf-8 _*_ 3 | # @Time    :2020/4/2 18:13 4 | # @Author  :Abner Wong 5 | # @Software: PyCharm 6 | 7 | import jieba.posseg as pseg 8 | 9 | import re 10 | from .const import STOPWORDS, PROPERTY_FILTER 11 | 12 | SENTENCE_SP_PATTERN = re.compile(r"。|\.|！|\!|？|\?|;") 13 | 14 | 15 | class Text: 16 | def __init__(self, text, use_property, no_stopwords): 17 | """ 18 | 19 | :param text: 20 | :param use_property: 是否根据词性进行筛选 21 | :param no_stopwords: 是否去停用词 22 | """ 23 | 24 | if not isinstance(text, str): 25 | raise ValueError('text type must be str!') 26 | elif text is None: 27 | raise ValueError('text should not be none!') 28 | 29 | self.sents = self._sentence_split(text) 30 | self.words_pro = self._get_words(self.sents, use_property, no_stopwords) 31 | 32 | def _get_words(self, sents, use_property, no_stopwords): 33 | 34 | words = list() 35 | 36 | if len(sents) < 1: 37 | return None 38 | 39 | for s in sents: 40 | cut_s = pseg.cut(s) 41 | if use_property: 42 | cut_s = [w for w in cut_s if w.flag in PROPERTY_FILTER] 43 | else: 44 | cut_s = [w for w in cut_s] 45 | 46 | cut_s = self._clean_words(cut_s) 47 | if no_stopwords: 48 | cut_s = [w.strip() for w in cut_s if w.strip() not in STOPWORDS] 49 | words.append(cut_s) 50 | 51 | return words 52 | 53 | @staticmethod 54 | def _sentence_split(text): 55 | sents = [i.strip() for i in SENTENCE_SP_PATTERN.split(text) if i != '' ] 56 | return sents 57 | 58 | @staticmethod 59 | def _clean_words(sent): 60 | w_ls = [w.word.strip() for w in sent if w.flag != 'x'] 61 | w_ls = [word for word in w_ls if len(word) > 0] 62 | return w_ls 63 | 64 | -------------------------------------------------------------------------------- /TextRank/textRank.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # _*_coding:utf-8 _*_ 3 | # @Time    :2020/4/2 19:36 4 | # @Author  :Abner Wong 5 | # @Software: PyCharm 6 | 7 | from .textPro import Text 8 | from . import utils 9 | 10 | from collections import Counter 11 | 12 | 13 | class TextRank(Text): 14 | 15 | def __init__(self, 16 | text=None, windows=2, 17 | use_property=True, no_stopwords=True, 18 | pr_config={'alpha': 0.85, 'max_iter': 100}): 19 | super(TextRank, self).__init__(text, use_property, no_stopwords) 20 | 21 | self.pr_config = pr_config 22 | self.windows = windows 23 | self.word_index, self.index_word, self.word_num = utils.words_info(self.words_pro) 24 | self.sorted_words = self._score_items(is_word=True) 25 | self.sorted_sents = self._score_items(is_word=False) 26 | 27 | def _build_adjacency_matrix(self, is_word=True): 28 | 29 | if is_word: 30 | adj_matrix = utils.word_adj_matrix(self.words_pro, 31 | self.windows, 32 | self.word_num, 33 | self.word_index) 34 | else: 35 | adj_matrix = utils.sent_adj_matrix(self.words_pro) 36 | 37 | return adj_matrix 38 | 39 | def _score_items(self, is_word=True): 40 | if is_word: 41 | adj_matrix = self._build_adjacency_matrix(is_word=is_word) 42 | scores = utils.cal_score(adj_matrix, **self.pr_config) 43 | sorted_items = utils.get_sorted_items(scores, self.index_word) 44 | else: 45 | adj_matrix = self._build_adjacency_matrix(is_word=is_word) 46 | scores = utils.cal_score(adj_matrix, **self.pr_config) 47 | index_sent = dict(zip(range(len(self.sents)), self.sents)) 48 | sorted_items = utils.get_sorted_items(scores, index_sent) 49 | 50 | return sorted_items 51 | 52 | def get_n_keywords(self, N): 53 | return self.sorted_words[:N] 54 | 55 | def get_n_sentences(self, N): 56 | return self.sorted_sents[:N] 57 | -------------------------------------------------------------------------------- /TextRank/utils.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # _*_coding:utf-8 _*_ 3 | # @Time    :2020/4/2 19:39 4 | # @Author  :Abner Wong 5 | # @Software: PyCharm 6 | 7 | import numpy as np 8 | import math 9 | 10 | 11 | def words_info(words_list): 12 | """ 13 | :param words_list: 14 | :return: 15 | """ 16 | word_index = dict() 17 | index_word = dict() 18 | word_num = 0 19 | 20 | for index_s, words in enumerate(words_list): 21 | for index_w, word in enumerate(words): 22 | word_index[word] = word_num 23 | index_word[word_num] = word 24 | word_num += 1 25 | 26 | return word_index, index_word, word_num 27 | 28 | 29 | def sents_info(sents_list): 30 | 31 | return dict(zip(range(len(sents_list)), sents_list)) 32 | 33 | 34 | def word_adj_matrix(words_pro, windows, word_num, word_index): 35 | """ 36 | Adjacency Matrix 37 | :param windows: 38 | :return: 39 | """ 40 | def _word_combine(words, window): 41 | """ 42 | Keyword arguments: 43 | :param window: 44 | """ 45 | if window < 2: window = 2 46 | 47 | for x in range(1, window): 48 | if x >= len(words): 49 | break 50 | words2 = words[x:] 51 | res = zip(words, words2) 52 | for r in res: 53 | yield r 54 | 55 | matrix = np.zeros((word_num, word_num)) 56 | 57 | for words in words_pro: 58 | for w1, w2 in _word_combine(words, windows): 59 | if w1 in word_index and w2 in word_index: 60 | index1 = word_index.get(w1) 61 | index2 = word_index.get(w2) 62 | matrix[index1][index2] = 1.0 63 | matrix[index2][index1] = 1.0 64 | 65 | return matrix 66 | 67 | 68 | def sent_adj_matrix(words_pro): 69 | 70 | def _get_similarity(word_ls1, word_ls2): 71 | co_occur_num = len(set(word_ls1) & set(word_ls2)) 72 | if abs(co_occur_num) <= 1e-12: 73 | return 0. 74 | 75 | if len(word_ls1) == 0 or len(word_ls2) == 0: 76 | return 0. 77 | 78 | denominator = math.log(float(len(word_ls1))) + math.log(float(len(word_ls2))) 79 | 80 | if abs(denominator) < 1e-12: 81 | return 0. 82 | 83 | return co_occur_num / denominator 84 | 85 | sentences_num = len(words_pro) 86 | matrix = np.zeros((sentences_num, sentences_num)) 87 | 88 | for x in range(sentences_num): 89 | for y in range(x, sentences_num): 90 | s = _get_similarity(words_pro[x], words_pro[y]) 91 | matrix[x, y] = s 92 | matrix[y, x] = s 93 | return matrix 94 | 95 | 96 | def cal_score(ad_matrix, alpha=0.85, max_iter=100): 97 | 98 | N = len(ad_matrix) 99 | ad_sum = ad_matrix.sum(axis=0).astype(float) 100 | ad_sum[ad_sum == 0.0] = 0.001 101 | ad_matrix = ad_matrix / ad_sum 102 | pr = np.full([N, 1], 1 / N) 103 | 104 | for _ in range(max_iter): 105 | pr = np.dot(ad_matrix, pr) * alpha + (1 - alpha) 106 | pr = pr / pr.sum() 107 | 108 | scores = dict(zip(range(len(pr)), [i[0] for i in pr])) 109 | 110 | return scores 111 | 112 | 113 | def get_sorted_items(scores, index_items): 114 | """ 115 | :param scores: 116 | :param index_items: 117 | :return: list[tuple] 118 | """ 119 | items_scores = dict() 120 | for index, score in scores.items(): 121 | items_scores[index_items.get(index)] = score 122 | sorted_items = sorted(items_scores.items(), key=lambda item: item[1], reverse=True) 123 | return sorted_items 124 | 125 | 126 | 127 | -------------------------------------------------------------------------------- /TextRank/statics/stopwords.txt: -------------------------------------------------------------------------------- 1 | ? 2 | 、 3 | 。 4 | “ 5 | ” 6 | 《 7 | 》 8 | ！ 9 | ， 10 | ： 11 | ； 12 | ？ 13 | 啊 14 | 阿 15 | 哎 16 | 哎呀 17 | 哎哟 18 | 唉 19 | 俺 20 | 俺们 21 | 按 22 | 按照 23 | 吧 24 | 吧哒 25 | 把 26 | 罢了 27 | 被 28 | 本 29 | 本着 30 | 比 31 | 比方 32 | 比如 33 | 鄙人 34 | 彼 35 | 彼此 36 | 边 37 | 别 38 | 别的 39 | 别说 40 | 并 41 | 并且 42 | 不比 43 | 不成 44 | 不单 45 | 不但 46 | 不独 47 | 不管 48 | 不光 49 | 不过 50 | 不仅 51 | 不拘 52 | 不论 53 | 不怕 54 | 不然 55 | 不如 56 | 不特 57 | 不惟 58 | 不问 59 | 不只 60 | 朝 61 | 朝着 62 | 趁 63 | 趁着 64 | 乘 65 | 冲 66 | 除 67 | 除此之外 68 | 除非 69 | 除了 70 | 此 71 | 此间 72 | 此外 73 | 从 74 | 从而 75 | 打 76 | 待 77 | 但 78 | 但是 79 | 当 80 | 当着 81 | 到 82 | 得 83 | 的 84 | 的话 85 | 等 86 | 等等 87 | 地 88 | 第 89 | 叮咚 90 | 对 91 | 对于 92 | 多 93 | 多少 94 | 而 95 | 而况 96 | 而且 97 | 而是 98 | 而外 99 | 而言 100 | 而已 101 | 尔后 102 | 反过来 103 | 反过来说 104 | 反之 105 | 非但 106 | 非徒 107 | 否则 108 | 嘎 109 | 嘎登 110 | 该 111 | 赶 112 | 个 113 | 各 114 | 各个 115 | 各位 116 | 各种 117 | 各自 118 | 给 119 | 根据 120 | 跟 121 | 故 122 | 故此 123 | 固然 124 | 关于 125 | 管 126 | 归 127 | 果然 128 | 果真 129 | 过 130 | 哈 131 | 哈哈 132 | 呵 133 | 和 134 | 何 135 | 何处 136 | 何况 137 | 何时 138 | 嘿 139 | 哼 140 | 哼唷 141 | 呼哧 142 | 乎 143 | 哗 144 | 还是 145 | 还有 146 | 换句话说 147 | 换言之 148 | 或 149 | 或是 150 | 或者 151 | 极了 152 | 及 153 | 及其 154 | 及至 155 | 即 156 | 即便 157 | 即或 158 | 即令 159 | 即若 160 | 即使 161 | 几 162 | 几时 163 | 己 164 | 既 165 | 既然 166 | 既是 167 | 继而 168 | 加之 169 | 假如 170 | 假若 171 | 假使 172 | 鉴于 173 | 将 174 | 较 175 | 较之 176 | 叫 177 | 接着 178 | 结果 179 | 借 180 | 紧接着 181 | 进而 182 | 尽 183 | 尽管 184 | 经 185 | 经过 186 | 就 187 | 就是 188 | 就是说 189 | 据 190 | 具体地说 191 | 具体说来 192 | 开始 193 | 开外 194 | 靠 195 | 咳 196 | 可 197 | 可见 198 | 可是 199 | 可以 200 | 况且 201 | 啦 202 | 来 203 | 来着 204 | 离 205 | 例如 206 | 哩 207 | 连 208 | 连同 209 | 两者 210 | 了 211 | 临 212 | 另 213 | 另外 214 | 另一方面 215 | 论 216 | 嘛 217 | 吗 218 | 慢说 219 | 漫说 220 | 冒 221 | 么 222 | 每 223 | 每当 224 | 们 225 | 莫若 226 | 某 227 | 某个 228 | 某些 229 | 拿 230 | 哪 231 | 哪边 232 | 哪儿 233 | 哪个 234 | 哪里 235 | 哪年 236 | 哪怕 237 | 哪天 238 | 哪些 239 | 哪样 240 | 那 241 | 那边 242 | 那儿 243 | 那个 244 | 那会儿 245 | 那里 246 | 那么 247 | 那么些 248 | 那么样 249 | 那时 250 | 那些 251 | 那样 252 | 乃 253 | 乃至 254 | 呢 255 | 能 256 | 你 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