Text summarization
Text summarization is the process of generating a concise and coherent summary of a longer text document, such as a news article or research paper. There are two main approaches to text summarization: extractive summarization and abstractive summarization.
Extractive summarization involves selecting a subset of the most important sentences or phrases from the original document and using them to create a summary. This approach is simpler and more interpretable, but it can be limited by the quality of the original document and may result in a summary that lacks coherence.
Abstractive summarization involves generating new text that summarizes the key information in the original document. This approach is more flexible and can produce more coherent summaries, but it can be more challenging and may require more complex models.
Here's an example of how to perform extractive summarization using Python and the nltk library:
import nltk
from nltk.corpus import reuters
from heapq import nlargest
def summarize_text(text, n):
# Tokenize the text into sentences
sentences = nltk.sent_tokenize(text)
# Calculate the score of each sentence based on the frequency of its words
word_freq = {}
for sentence in sentences:
for word in nltk.word_tokenize(sentence.lower()):
if word not in word_freq:
word_freq[word] = 1
else:
word_freq[word] += 1
max_freq = max(word_freq.values())
for word in word_freq.keys():
word_freq[word] /= max_freq
# Select the top n sentences with the highest score
sentence_scores = {}
for sentence in sentences:
for word in nltk.word_tokenize(sentence.lower()):
if word in word_freq:
if len(sentence.split(' ')) < 30:
if sentence not in sentence_scores:
sentence_scores[sentence] = word_freq[word]
else:
sentence_scores[sentence] += word_freq[word]
top_sentences = nlargest(n, sentence_scores, key=sentence_scores.get)
# Combine the top sentences into a summary
summary = ' '.join(top_sentences)
return summary
In this example, we first tokenize the input text into sentences using the sent_tokenize function from nltk. We then calculate the frequency of each word in the text and normalize the frequencies to ensure that longer sentences are not favored. We score each sentence by summing the normalized frequencies of its words, and then select the top n sentences with the highest score using the nlargest function from the heapq module. Finally, we combine the selected sentences into a summary and return it.
This approach can be extended or modified in various ways, such as using more sophisticated algorithms for scoring the sentences or incorporating other features such as named entities or topic models.
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