| |
| |
| |
| |
| """ |
| Summarization by frequency of words |
| """ |
| from collections import defaultdict |
| from heapq import nlargest |
| from string import punctuation |
| from typing import List |
|
|
| from pythainlp.corpus import thai_stopwords |
| from pythainlp.tokenize import sent_tokenize, word_tokenize |
|
|
| _STOPWORDS = thai_stopwords() |
|
|
|
|
| class FrequencySummarizer: |
| def __init__(self, min_cut: float = 0.1, max_cut: float = 0.9): |
| self.__min_cut = min_cut |
| self.__max_cut = max_cut |
| self.__stopwords = set(punctuation).union(_STOPWORDS) |
|
|
| @staticmethod |
| def __rank(ranking, n: int): |
| return nlargest(n, ranking, key=ranking.get) |
|
|
| def __compute_frequencies( |
| self, word_tokenized_sents: List[List[str]] |
| ) -> defaultdict: |
| word_freqs = defaultdict(int) |
| for sent in word_tokenized_sents: |
| for word in sent: |
| if word not in self.__stopwords: |
| word_freqs[word] += 1 |
|
|
| max_freq = float(max(word_freqs.values())) |
| for w in list(word_freqs): |
| word_freqs[w] = word_freqs[w] / max_freq |
| if ( |
| word_freqs[w] >= self.__max_cut |
| or word_freqs[w] <= self.__min_cut |
| ): |
| del word_freqs[w] |
|
|
| return word_freqs |
|
|
| def summarize( |
| self, text: str, n: int, tokenizer: str = "newmm" |
| ) -> List[str]: |
| sents = sent_tokenize(text, engine="whitespace+newline") |
| word_tokenized_sents = [ |
| word_tokenize(sent, engine=tokenizer) for sent in sents |
| ] |
| self.__freq = self.__compute_frequencies(word_tokenized_sents) |
| ranking = defaultdict(int) |
|
|
| for i, sent in enumerate(word_tokenized_sents): |
| for w in sent: |
| if w in self.__freq: |
| ranking[i] += self.__freq[w] |
| summaries_idx = self.__rank(ranking, n) |
|
|
| return [sents[j] for j in summaries_idx] |
|
|
