Text Summarization

Text summarization is the task of creating concise and coherent summaries of longer documents while preserving key information and overall meaning. It can be either extractive (selecting existing sentences) or abstractive (generating new text).

Core Concepts

Types of Summarization

  1. Extractive Summarization:

    • Selects important sentences from source
    • Maintains original wording
    • Easier to implement and evaluate

  2. Abstractive Summarization:

    • Generates new text
    • Requires language understanding
    • More human-like summaries

Implementation Approaches

1. Extractive Summarization

from nltk.tokenize import sent_tokenize
from nltk.corpus import stopwords
from sklearn.feature_extraction.text import TfidfVectorizer

class ExtractiveSummarizer:
    def __init__(self):
        self.stop_words = set(stopwords.words('english'))
        self.vectorizer = TfidfVectorizer(stop_words='english')
        
    def summarize(self, text, num_sentences=3):
        # Split into sentences
        sentences = sent_tokenize(text)
        
        # Calculate sentence scores
        tfidf_matrix = self.vectorizer.fit_transform(sentences)
        sentence_scores = tfidf_matrix.sum(axis=1).A1
        
        # Select top sentences
        top_indices = sentence_scores.argsort()[-num_sentences:]
        summary = ' '.join([sentences[i] for i in sorted(top_indices)])
        
        return summary

2. Abstractive Summarization

from transformers import (
    BartForConditionalGeneration,
    BartTokenizer
)

def abstractive_summarize(text):
    tokenizer = BartTokenizer.from_pretrained('facebook/bart-large-cnn')
    model = BartForConditionalGeneration.from_pretrained(
        'facebook/bart-large-cnn'
    )
    
    inputs = tokenizer(text, 
                      return_tensors='pt', 
                      max_length=1024, 
                      truncation=True)
    
    summary_ids = model.generate(
        inputs['input_ids'],
        max_length=150,
        min_length=40,
        length_penalty=2.0,
        num_beams=4,
        early_stopping=True
    )
    
    summary = tokenizer.decode(summary_ids[0], 
                             skip_special_tokens=True)
    return summary

Advanced Features

1. Topic-Based Summarization

from gensim.summarization import summarize
from gensim.models import LdaModel
from gensim.corpora import Dictionary

def topic_based_summary(text, num_topics=3):
    # Preprocess text
    sentences = sent_tokenize(text)
    words = [preprocess(sent) for sent in sentences]
    
    # Create dictionary and corpus
    dictionary = Dictionary(words)
    corpus = [dictionary.doc2bow(word) for word in words]
    
    # Train LDA model
    lda = LdaModel(corpus, 
                   num_topics=num_topics, 
                   id2word=dictionary)
    
    # Get topic distribution for each sentence
    sentence_topics = [lda[doc] for doc in corpus]
    
    # Select sentences based on topic coverage
    selected = select_diverse_sentences(sentences, 
                                      sentence_topics)
    return ' '.join(selected)

2. Multi-Document Summarization

class MultiDocSummarizer:
    def __init__(self):
        self.single_doc_summarizer = ExtractiveSummarizer()
        
    def summarize(self, documents, summary_length=200):
        # Get individual summaries
        summaries = [
            self.single_doc_summarizer.summarize(doc)
            for doc in documents
        ]
        
        # Combine and remove redundancy
        combined = self.merge_summaries(summaries)
        
        # Trim to desired length
        return self.trim_summary(combined, summary_length)

Best Practices

1. Preprocessing

  • Clean input text
  • Handle special characters
  • Normalize content

2. Model Selection

  • Consider input length
  • Evaluate output requirements
  • Balance quality and speed

3. Evaluation

  • Use ROUGE metrics
  • Human evaluation
  • Check for factual accuracy

Implementation Example

class HybridSummarizer:
    def __init__(self, mode='auto'):
        self.mode = mode
        self.extractive = ExtractiveSummarizer()
        self.abstractive = BartForConditionalGeneration.from_pretrained(
            'facebook/bart-large-cnn'
        )
        self.tokenizer = BartTokenizer.from_pretrained(
            'facebook/bart-large-cnn'
        )
    
    def summarize(self, text, max_length=150):
        if self.mode == 'auto':
            # Choose method based on text length
            if len(text.split()) > 1000:
                return self.extractive_summarize(text)
            else:
                return self.abstractive_summarize(text)
        elif self.mode == 'extractive':
            return self.extractive_summarize(text)
        else:
            return self.abstractive_summarize(text)
    
    def extractive_summarize(self, text):
        return self.extractive.summarize(text)
    
    def abstractive_summarize(self, text):
        inputs = self.tokenizer(text, 
                              return_tensors='pt', 
                              max_length=1024, 
                              truncation=True)
        
        summary_ids = self.abstractive.generate(
            inputs['input_ids'],
            max_length=150,
            min_length=40,
            length_penalty=2.0,
            num_beams=4,
            early_stopping=True
        )
        
        return self.tokenizer.decode(summary_ids[0], 
                                   skip_special_tokens=True)

Applications

  1. Content Curation:

    • News summarization
    • Document abstraction
    • Research paper summaries

  2. Information Management:

    • Meeting notes
    • Email digests
    • Report generation

  3. Knowledge Discovery:

    • Literature review
    • Trend analysis
    • Content aggregation

Challenges

  1. Content Selection:

    • Identifying key information
    • Maintaining coherence
    • Handling redundancy

  2. Quality Control:

    • Factual accuracy
    • Grammatical correctness
    • Semantic consistency

  3. Domain Adaptation:

    • Technical content
    • Multiple languages
    • Domain-specific terminology

Summary

Text summarization is a complex task that requires balancing information retention with conciseness. Modern approaches, particularly neural models, have significantly improved summarization quality, though challenges remain in ensuring factual accuracy and domain adaptation.