Abstract
Extractive summarization is a natural language processing (NLP) technique that generates a concise summary by selecting and combining the most informative sentences or phrases directly from the original text without altering their wording. This technique has gained significant attention in recent years due to its ability to provide a compressed version of a large document, retaining the key points and essential information. In this paper, we provide an in-depth analysis of extractive summarization, its techniques, and applications. We also discuss the challenges and limitations associated with this technique and provide future directions for research.
Introduction
The volume of text data available today is overwhelming, and it is becoming increasingly difficult to manually summarize large documents, articles, and books. Automatic summarization techniques have been developed to address this issue, and extractive summarization is one of the most popular approaches. Extractive summarization involves selecting a subset of sentences or phrases from the original text and combining them to form a concise summary. The goal of extractive summarization is to retain the most important information from the original text while reducing its size.
For example, if a long article discusses climate change impacts, an extractive summarization method might select sentences such as “Global temperatures have increased by 1.2 degrees Celsius since the industrial revolution” and “Rising sea levels threaten coastal communities worldwide” directly from the text to form a brief summary without rephrasing or introducing new language.
While extractive summarization ensures accuracy and adherence to the original text, it may result in summaries that lack coherence and smooth flow since sentences are pulled verbatim and might not connect logically. This technique suits scenarios requiring precision and faithfulness, like legal or scientific documents, where content must remain unchanged.
In contrast, abstractive summarization paraphrases or rewrites the source material to create a more fluent and coherent summary, but this can risk introducing errors or deviating from the original meaning.
Techniques
Extractive summarization techniques can be broadly classified into two categories: supervised and unsupervised methods. Supervised methods involve training a machine learning model on a labeled dataset, where the labels indicate the importance of each sentence or phrase. The model learns to predict the importance of each sentence or phrase and selects the most important ones to include in the summary. Unsupervised methods, on the other hand, do not require labeled data and rely on statistical methods to determine the importance of each sentence or phrase.Extractive summarization is a natural language processing (NLP) technique that generates a concise summary by selecting and combining the most informative sentences or phrases directly from the original text without altering their wording. It works by analyzing the full source text, segmenting it into units like sentences, and then scoring these units based on criteria such as relevance, keyword frequency, sentence position, and similarity to the main topic. The highest-scoring sentences are extracted and concatenated to form the summary, preserving the original context and factual accuracy.
To summarize, extractive summarization:
- Selects key sentences or phrases directly from the source text.
- Scores segments based on factors like keyword frequency and sentence position.
- Combines these selected sentences to form a summary maintaining original wording.
Is ideal for accuracy-critical domains but may lack smooth narrative flow.
Example: From a news article, it might take the sentence “The president signed a new climate bill into law today” plus “The bill aims to reduce carbon emissions by 2030” and produce a summary composed of these exact sentences without modification.
This explanation covers how extractive summarization works, its pros and cons, and illustrates its practical use with examples.iterate+Python Code Example
Python Text Summarizer Code
Here is a step-by-step explanation of the extractive summarization Python code using NLTK:
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize, sent_tokenize
from nltk.probability import FreqDist
from heapq import nlargest import string
nltk.download('punkt')
nltk.download('stopwords')
def extractive_summarize(text, num_sentences=3): sentences = sent_tokenize(text)
stop_words = set(stopwords.words('english') + list(string.punctuation))
words = word_tokenize(text.lower())
filtered_words = [word for word in words if word not in stop_words]
freq_dist = FreqDist(filtered_words)
sentence_scores = {}
for sentence in sentences:
sentence_lower = sentence.lower()
sentence_words = word_tokenize(sentence_lower)
score = 0
for word in sentence_words:
if word in freq_dist:
score += freq_dist[word]
sentence_scores[sentence] = score
summary_sentences = nlargest(num_sentences, sentence_scores, key=sentence_scores.get)
summary = ' '.join(summary_sentences)
return summary
text = """Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers
and human language. In particular, how to program computers to process and analyze large amounts of natural language data. The result is a computer capable of "understanding"
the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the
documents as well as categorize and organize the documents themselves. Challenges in natural language processing frequently involve speech recognition, natural language
understanding, and natural language generation."""
summary = extractive_summarize(text, num_sentences=2)
print("Summary:") print(summary)- Import and Download Resources
The code imports essential NLTK modules:sent_tokenizeandword_tokenizefor splitting text into sentences and words,stopwordsfor common non-informative words, andFreqDistfor frequency distribution of words. It downloads necessary datasets for tokenization and stopwords. - Tokenizing Sentences
The input text is divided into individual sentences usingsent_tokenize(). This segmentation is crucial since the goal is to score and extract whole sentences for the summary. - Preprocessing Words
The entire text is tokenized into lowercase words viaword_tokenize(). Stopwords (common words like “the”, “and”) and punctuation are removed using a set of stopwords combined with string punctuation to keep only meaningful words related to the content. - Calculating Word Frequencies
The filtered words are passed toFreqDistwhich counts how many times each word appears across the entire text. These frequencies serve as weights representing word importance. - Scoring Sentences
Each sentence is scored by summing the frequencies of the words it contains. Sentences with more frequent important words get higher scores, assuming they hold more information. - Select Top Sentences
The highest scoring sentences (top N bynum_sentences) are extracted usingnlargest. These sentences are considered the most informative. - Form Summary
The selected sentences are concatenated with spaces to form the final extractive summary.
By following these steps, the code picks key sentences directly from the original text based on word importance, creating a concise and faithful summary without rephrasing or generating new content. This method balances simplicity and effectiveness for extractive summarization tasks with NLTK. heartbeat.
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- https://github.com/topics/extractive-text-summarization
