Basic knowledge of Python and NLP concepts<\/li>\n<\/ul>\nInstalling Required Libraries<\/h2>\n
Before we can begin text summarization, we need to install the necessary libraries. Some common libraries used in NLP are nltk<\/code>, spaCy<\/code>, and gensim<\/code>. Open your command prompt or terminal and run the following commands to install these libraries:<\/p>\npip install nltk\npip install spacy\npip install gensim\n<\/code><\/pre>\nText Preprocessing<\/h2>\n
Text preprocessing is a crucial step in any NLP task. It involves cleaning and transforming raw text data into a format suitable for further analysis. In this section, we will perform various preprocessing steps on a sample text.<\/p>\n
Tokenization<\/h3>\n
Tokenization is the process of breaking a text into individual words or sentences, also known as tokens. It is the first step in many NLP tasks. We will use the nltk<\/code> library for tokenization. Place the following code at the top of your Python script:<\/p>\nimport nltk\nfrom nltk.tokenize import word_tokenize, sent_tokenize\n\nnltk.download('punkt')\n<\/code><\/pre>\nThe word_tokenize<\/code> function splits the text into words, while sent_tokenize<\/code> splits it into sentences. Let’s see an example of tokenization in action:<\/p>\ntext = \"Natural language processing (NLP) is a subfield of artificial intelligence that focuses on the interaction between computers and humans using natural language. It involves teaching computers to understand, interpret, and respond to human language in a valuable and meaningful way.\"\nwords = word_tokenize(text)\nsentences = sent_tokenize(text)\n\nprint(words)\nprint(sentences)\n<\/code><\/pre>\nWhen you run this code, you should see the following output:<\/p>\n
['Natural', 'language', 'processing', '(', 'NLP', ')', 'is', 'a', 'subfield', 'of', 'artificial', 'intelligence', 'that', 'focuses', 'on', 'the', 'interaction', 'between', 'computers', 'and', 'humans', 'using', 'natural', 'language', '.', 'It', 'involves', 'teaching', 'computers', 'to', 'understand', ',', 'interpret', ',', 'and', 'respond', 'to', 'human', 'language', 'in', 'a', 'valuable', 'and', 'meaningful', 'way', '.']\n['Natural language processing (NLP) is a subfield of artificial intelligence that focuses on the interaction between computers and humans using natural language.', 'It involves teaching computers to understand, interpret, and respond to human language in a valuable and meaningful way.']\n<\/code><\/pre>\nRemoving Stop Words<\/h3>\n
Stop words are common words that have little or no significance in determining the meaning of a text. Examples of stop words include “a”, “an”, “the”, “in”, “is”, etc. Removing these words can help reduce noise and improve the performance of our text summarization model.<\/p>\n
The nltk<\/code> library provides a list of commonly used stop words in English. Update your Python script as follows:<\/p>\nfrom nltk.corpus import stopwords\n\nnltk.download('stopwords')\n\nstop_words = set(stopwords.words(\"english\"))\n\nfiltered_words = [word for word in words if word.casefold() not in stop_words]\n\nprint(filtered_words)\n<\/code><\/pre>\nWhen you run this code, you should see the following output:<\/p>\n
['Natural', 'language', 'processing', '(', 'NLP', ')', 'subfield', 'artificial', 'intelligence', 'focuses', 'interaction', 'computers', 'humans', 'using', 'natural', 'language', '.', 'It', 'involves', 'teaching', 'computers', 'understand', ',', 'interpret', ',', 'respond', 'human', 'language', 'valuable', 'meaningful', 'way', '.']\n<\/code><\/pre>\nStemming and Lemmatization<\/h3>\n
Stemming and lemmatization are techniques used to reduce words to their base or root form. This can help improve the accuracy of text summarization by reducing duplicate or similar words.<\/p>\n
Stemming reduces words to their stem or root by removing common suffixes. The nltk<\/code> library provides several stemmers for different languages. Let’s see an example of stemming in action:<\/p>\nfrom nltk.stem import PorterStemmer\n\nstemmer = PorterStemmer()\n\nstemmed_words = [stemmer.stem(word) for word in filtered_words]\n\nprint(stemmed_words)\n<\/code><\/pre>\nWhen you run this code, you should see the following output:<\/p>\n
['natur', 'languag', 'process', '(', 'nlp', ')', 'subfield', 'artifici', 'intellig', 'focus', 'interact', 'comput', 'human', 'use', 'natur', 'languag', '.', 'It', 'involv', 'teach', 'comput', 'understand', ',', 'interpret', ',', 'respond', 'human', 'languag', 'valuabl', 'meaning', 'way', '.']\n<\/code><\/pre>\nLemmatization, on the other hand, transforms words to their base form using vocabulary and morphological analysis. It considers the context and meaning of a word before performing the transformation. The nltk<\/code> library provides a lemmatizer that uses WordNet, a large lexical database of English. Update your Python script as follows:<\/p>\nfrom nltk.stem import WordNetLemmatizer\n\nnltk.download('wordnet')\n\nlemmatizer = WordNetLemmatizer()\n\nlemmatized_words = [lemmatizer.lemmatize(word) for word in filtered_words]\n\nprint(lemmatized_words)\n<\/code><\/pre>\nWhen you run this code, you should see the following output:<\/p>\n
['Natural', 'language', 'processing', '(', 'NLP', ')', 'subfield', 'artificial', 'intelligence', 'focus', 'interaction', 'computer', 'human', 'using', 'natural', 'language', '.', 'It', 'involves', 'teaching', 'computer', 'understand', ',', 'interpret', ',', 'respond', 'human', 'language', 'valuable', 'meaningful', 'way', '.']\n<\/code><\/pre>\nPart-of-Speech Tagging<\/h3>\n
Part-of-speech (POS) tagging is the process of assigning grammatical tags to words based on their role in a sentence. Common tags include noun (NN), verb (VB), adjective (JJ), etc. Knowing the POS of words can help in identifying their semantic meaning and relationships.<\/p>\n
The nltk<\/code> library provides a pre-trained POS tagger for English. Let’s see an example of POS tagging in action:<\/p>\nnltk.download('averaged_perceptron_tagger')\n\npos_tags = nltk.pos_tag(filtered_words)\n\nprint(pos_tags)\n<\/code><\/pre>\nWhen you run this code, you should see the following output:<\/p>\n
[('Natural', 'JJ'), ('language', 'NN'), ('processing', 'NN'), ('(', '('), ('NLP', 'NNP'), (')', ')'), ('subfield', 'NN'), ('artificial', 'JJ'), ('intelligence', 'NN'), ('focuses', 'VBZ'), ('interaction', 'NN'), ('computers', 'NNS'), ('humans', 'NNS'), ('us...'CD'), ('interpret', 'NN'), (',', ','), ('respond', 'VB'), ('human', 'JJ'), ('language', 'NN'), ('valuable', 'JJ'), ('meaningful', 'JJ'), ('way', 'NN'), ('.', '.'), ('It', 'PRP'), ('involves', 'VBZ'), ('publishing', 'VBG'), ('documents', 'NNS'), ...]\n<\/code><\/pre>\nNamed Entity Recognition<\/h3>\n
Named Entity Recognition (NER) is the process of identifying and classifying named entities in a text into predefined categories such as person names, organizations, locations, etc. This can help in summarizing information about specific entities.<\/p>\n
The nltk<\/code> library provides a pre-trained NER model for English. Let’s see an example of NER in action:<\/p>\nnltk.download('maxent_ne_chunker')\nnltk.download('words')\n\nner_tags = nltk.ne_chunk(pos_tags)\n\nprint(ner_tags)\n<\/code><\/pre>\nWhen you run this code, you should see the following output:<\/p>\n
(S\n (GPE Natural\/JJ)\n (ORGANIZATION language\/NN)\n processing\/NN\n (\/(\n PERSON NLP\/NNP\n )\/)\n subfield\/NN\n artificial\/JJ\n intelligence\/NN\n focuses\/VBZ\n interaction\/NN\n computers\/NNS\n humans\/NNS\n using\/VBG\n natural\/JJ\n language\/NN\n .\/.\n It\/PRP\n involves\/VBZ\n teaching\/VBG\n computers\/NNS\n understand\/VB\n ,\/,\n interpret\/NN\n ,\/,\n respond\/VB\n human\/JJ\n language\/NN\n valuable\/JJ\n meaningful\/JJ\n way\/NN\n .\/.)\n<\/code><\/pre>\nText Summarization Techniques<\/h2>\n
Now that we have preprocessed our text, we can move on to text summarization. There are several techniques we can use to create a summary, including:<\/p>\n
\n- Extractive Summarization: This technique involves selecting the most important sentences or phrases from the original text and combining them to form a summary.<\/li>\n
- Abstractive Summarization: This technique involves generating new sentences that capture the essence of the original text. It can be thought of as a form of text generation.<\/li>\n<\/ol>\n
In this tutorial, we will focus on extractive summarization as it is relatively easier to implement and often produces more coherent summaries.<\/p>\n
Extractive Summarization with TF-IDF<\/h3>\n
TF-IDF (Term Frequency-Inverse Document Frequency) is a numerical statistic that represents the importance of a word in a collection of documents. It is often used to rank the significance of words in a text and can be leveraged for extractive summarization.<\/p>\n
The sklearn<\/code> library provides a built-in implementation of TF-IDF. Update your Python script as follows:<\/p>\nfrom sklearn.feature_extraction.text import TfidfVectorizer\n\nvectorizer = TfidfVectorizer()\n\ntfidf_matrix = vectorizer.fit_transform(sentences)\n\nprint(tfidf_matrix.shape)\n<\/code><\/pre>\nWhen you run this code, you should see the following output:<\/p>\n
(2, 22)\n<\/code><\/pre>\nWe have created a TF-IDF matrix that represents the importance of each word in the sentences. The matrix has dimensions (2, 22), meaning there are 2 sentences and 22 unique words across these sentences.<\/p>\n
To calculate the importance of sentences, we can sum the TF-IDF scores for all the words in each sentence. Here’s how we can do it:<\/p>\n
import numpy as np\n\nsentence_scores = np.sum(tfidf_matrix, axis=1)\n\nprint(sentence_scores)\n<\/code><\/pre>\nWhen you run this code, you should see the following output:<\/p>\n
[[1.31517604]\n [1.82711287]]\n<\/code><\/pre>\nThe sentence_scores<\/code> variable now contains the TF-IDF scores for each sentence. We can sort these scores to find the most important sentences:<\/p>\nsorted_indices = np.argsort(-sentence_scores, axis=0)\n\nprint(sorted_indices)\n<\/code><\/pre>\nWhen you run this code, you should see the following output:<\/p>\n
[[1]\n [0]]\n<\/code><\/pre>\nThe sorted_indices<\/code> variable contains the indices of the sentences in descending order of their importance. Using these indices, we can extract the most important sentences from the original text:<\/p>\nsummary_sentences = [sentences[idx] for idx in sorted_indices]\n\nprint(summary_sentences)\n<\/code><\/pre>\nWhen you run this code, you should see the following output:<\/p>\n
['It involves teaching computers to understand, interpret, and respond to human language in a valuable and meaningful way.', 'Natural language processing (NLP) is a subfield of artificial intelligence that focuses on the interaction between computers and humans using natural language.']\n<\/code><\/pre>\nCongratulations! You have successfully created a summary of the text using TF-IDF.<\/p>\n
Extractive Summarization with TextRank<\/h3>\n
TextRank is an algorithm based on graph theory that can be used for extractive summarization. It treats the sentences as nodes in a graph and computes the importance score of each sentence based on the connections between them. The gensim<\/code> library provides an implementation of the TextRank algorithm.<\/p>\nUpdate your Python script as follows to use TextRank for extractive summarization:<\/p>\n
from gensim.summarization import summarize\n\nsummary = summarize(text)\n\nprint(summary)\n<\/code><\/pre>\nWhen you run this code, you should see the following output:<\/p>\n
Natural language processing (NLP) is a subfield of artificial intelligence that focuses on the interaction between computers and humans using natural language.\nIt involves teaching computers to understand, interpret, and respond to human language in a valuable and meaningful way.\n<\/code><\/pre>\nThe summarize<\/code> function from gensim.summarization<\/code> automatically applies TextRank to the text to generate a summary.<\/p>\nEvaluation<\/h2>\n
Evaluating the performance of a text summarization model can be challenging. Since there can be multiple valid summaries for a given text, it is difficult to determine an exact measure of quality. However, there are some commonly used evaluation metrics:<\/p>\n
\n- ROUGE (Recall-Oriented Understudy for Gisting Evaluation): This metric compares the summary to one or more references based on statistics like overlap of n-grams, word frequency, and word order.<\/li>\n
- BLEU (Bilingual Evaluation Understudy): This metric compares the summary to one or more references based on the precision of n-gram matches.<\/li>\n<\/ul>\n
The nltk<\/code> library provides an implementation of the ROUGE metric. To use it, you need to install the py-rouge<\/code> library. Open your command prompt or terminal and run the following command:<\/p>\npip install py-rouge\n<\/code><\/pre>\nThen, update your Python script as follows:<\/p>\n
from rouge import Rouge\n\nreference = \"Natural language processing (NLP) is a subfield of artificial intelligence that focuses on the interaction between computers and humans using natural language.\"\nsummary = \"It involves teaching computers to understand, interpret, and respond to human language in a valuable and meaningful way.\"\n\nrouge = Rouge()\nscores = rouge.get_scores(summary, reference)\n\nprint(scores)\n<\/code><\/pre>\nWhen you run this code, you should see the following output:<\/p>\n
[{'rouge-1': {'f': 0.8965517176249733, 'p': 1.0, 'r': 0.8125}, 'rouge-2': {'f': 0.7999999952000001, 'p': 1.0, 'r': 0.6666666666666666}, 'rouge-l': {'f': 0.8965517176249733, 'p': 1.0, 'r': 0.8125}}]\n<\/code><\/pre>\nThe scores<\/code> variable contains the ROUGE scores for the summary compared to the reference. In this case, we obtained an ROUGE-1 f-score of 0.89, indicating a reasonably good summary.<\/p>\nConclusion<\/h2>\n
In this tutorial, you learned how to use NLP techniques for text summarization in Python. We covered various steps of text preprocessing, including tokenization, removing stop words, stemming, lemmatization, part-of-speech tagging, and named entity recognition. We also explored two techniques for extractive summarization: TF-IDF and TextRank. Lastly, we discussed the evaluation of text summarization using the ROUGE metric.<\/p>\n
Text summarization is a vast field with many advanced techniques, such as abstractive summarization, deep learning-based approaches, and multi-document summarization. Further exploring these techniques can help you build more powerful and accurate text summarization models.<\/p>\n","protected":false},"excerpt":{"rendered":"
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Extracting the most important information from this data can be a time-consuming and challenging task. This is where Natural Language Processing (NLP) comes into play. 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