Text summarization and generation are two powerful NLP applications that help manage information overload and create human-like text content. This chapter explores how to implement these techniques using Python, covering both extractive and abstractive summarization methods, as well as various text generation approaches from template-based systems to advanced neural models.

Introduction to Text Summarization

Text summarization condenses a longer document into a shorter version while preserving its key information and meaning. There are two main approaches:

1. Extractive Summarization: Identifies and extracts important sentences from the original text without modifying them. 2. Abstractive Summarization: Generates new sentences that capture the essence of the original text, similar to how humans create summaries.

Let's start by exploring extractive summarization techniques.

Extractive Summarization

Basic Frequency-Based Summarization

One of the simplest approaches to extractive summarization is based on word frequency:

summary = simple_extractive_summarizer(text, num_sentences=2) print("Original Text Length:", len(text)) print("Summary Length:", len(summary)) print(" Summary:") print(summary)

TextRank Algorithm for Summarization

TextRank is a graph-based algorithm inspired by Google's PageRank, which ranks sentences based on their similarity to other sentences:

textrank_summary = textrank_summarizer(text, num_sentences=2) print(" TextRank Summary:") print(textrank_summary) print(" TextRank Summary Length:", len(textrank_summary))

Using Gensim for Summarization

Gensim provides a simple interface for text summarization:

try: gensim_summary = gensim_summarizer(text, ratio=0.3) print(" Gensim Summary:") print(gensim_summary) print(" Gensim Summary Length:", len(gensim_summary)) except ImportError: print(" Gensim summarization module not available.") print("Install with: pip install gensim==3.8.3 (for summarize function)")

Using Sumy for Summarization

Sumy is a Python library that implements various extractive summarization algorithms:

Evaluating Extractive Summaries

ROUGE (Recall-Oriented Understudy for Gisting Evaluation) is a common metric for evaluating summaries:

Abstractive Summarization

Abstractive summarization generates new sentences that capture the essence of the original text. This approach typically uses deep learning models, particularly sequence-to-sequence architectures.

Using Transformers for Abstractive Summarization

The Hugging Face Transformers library provides pre-trained models for abstractive summarization:

Building a Custom Abstractive Summarizer with PyTorch

For more control, you can build a custom abstractive summarizer using PyTorch:

print(" Building a custom abstractive summarizer with PyTorch requires:") print("1. An encoder-decoder architecture with attention") print("2. A dataset of document-summary pairs") print("3. Training infrastructure (GPUs recommended)") print("4. Evaluation metrics like ROUGE") print(" The code example above shows the structure of such a model but is not executed.")

Text Generation

Text generation involves creating new text that is coherent, contextually relevant, and often indistinguishable from human-written text. Let's explore various approaches to text generation.

Markov Chain Text Generation

Markov chains are a simple but effective approach for generating text:

print(" Markov Chain Generated Text:") print(generated_text)

N-gram Language Models

N-gram models predict the next word based on the previous N-1 words:

print(" N-gram Generated Text:") print(ngram_generated_text) except Exception as e: print(f" Error training n-gram model: {e}") print("Make sure NLTK is properly installed with: pip install nltk")

Recurrent Neural Networks (RNNs) for Text Generation

RNNs can capture longer-term dependencies in text:

print(" Recurrent Neural Networks (RNNs) for text generation:") print("1. Tokenize and preprocess text data") print("2. Create sequences for training") print("3. Build an RNN model (LSTM or GRU)") print("4. Train the model to predict the next word") print("5. Generate text by repeatedly predicting the next word") print(" The code example above shows the structure of such a model but is not executed.")

Transformer-Based Text Generation

Transformer models like GPT have revolutionized text generation:

Fine-tuning GPT-2 for Domain-Specific Text Generation

For domain-specific text generation, you can fine-tune GPT-2 on your own data:

print(" Fine-tuning GPT-2 for domain-specific text generation:") print("1. Prepare domain-specific training data") print("2. Load pre-trained GPT-2 model and tokenizer") print("3. Create a dataset and data collator") print("4. Set up training arguments and trainer") print("5. Fine-tune the model on your data") print("6. Generate text using the fine-tuned model") print(" The code example above shows the process but is not executed.")

Practical Applications

Automatic Document Summarization

Let's build a simple application that summarizes a document using multiple techniques:

for method, summary in summaries.items(): print(f" {method.upper()} Summary:") print(summary) print(f"Length: {len(summary)}")

Automated Content Generation

Let's create a simple application that generates content based on a topic:

# Try GPT-2 if available if 'generate_text_gpt2' in globals(): print(f" GPT-2 Generated Content on {topic}:") gpt2_content = generate_content(topic, length=100, method='gpt2') print(gpt2_content)

Evaluation of Generated Text

Evaluating generated text is challenging but essential for improving models:

if 'generated_text' in locals(): print(" Evaluation of Markov Chain Generated Text:") metrics = evaluate_generated_text(generated_text) for metric, value in metrics.items(): print(f"{metric}: {value}")

Conclusion

Text summarization and generation are powerful NLP applications with numerous practical uses. This chapter covered:

1. Extractive Summarization: - Frequency-based methods - Graph-based algorithms like TextRank - Libraries like Gensim and Sumy

2. Abstractive Summarization: - Transformer-based models like BART and T5 - Custom sequence-to-sequence architectures

3. Text Generation: - Markov chains and n-gram models - Recurrent Neural Networks (RNNs) - Transformer models like GPT-2 - Fine-tuning for domain-specific generation

4. Practical Applications: - Document summarization - Automated content generation - Evaluation metrics for generated text

These techniques enable a wide range of applications, from summarizing news articles and research papers to generating creative content, product descriptions, and conversational responses. As models continue to improve, we can expect even more sophisticated summarization and generation capabilities that further blur the line between human and machine-generated text.

Practice exercises: 1. Compare different extractive summarization techniques on a corpus of news articles 2. Implement an abstractive summarizer using a pre-trained transformer model 3. Fine-tune GPT-2 on a domain-specific corpus (e.g., scientific papers, legal documents) 4. Build a content generation system that combines template-based and neural approaches 5. Develop evaluation metrics that assess both the fluency and factual accuracy of generated text