[AI from Scratch] Episode 262: Basics of Text Summarization

Recap and Today’s Theme

Hello! In the previous episode, we covered Seq2Seq models for translation, explaining how they transform sequences into other sequences using encoders and decoders, widely used in machine translation.

Today, we will explore the basics of text summarization, a technique for condensing long texts while extracting key information. Text summarization is applied in news summarization, report creation, and other fields. This episode covers the fundamental concepts, summarization methods, and implementation examples.

What is Text Summarization?

1. Basic Concept of Text Summarization

Text Summarization is the technique of extracting essential information from long texts and condensing it into shorter summaries. This allows users to efficiently grasp the content, saving time. There are two primary approaches to text summarization:

• Extractive Summarization: Extracts key sentences or phrases directly from the original text to form the summary.
• Abstractive Summarization: Generates new sentences based on the content of the original text to create a summary.

2. Applications of Summarization

Text summarization is widely used in various fields:

• News Article Summarization: Condenses long news articles into brief summaries to highlight key points.
• Academic and Report Summarization: Summarizes the main points of research papers, supporting efficient research.
• Meeting Minutes Summarization: Concisely summarizes meeting records, extracting critical decisions and action items.

Extractive Summarization

1. Basics of Extractive Summarization

Extractive Summarization involves selecting and compiling sentences or phrases directly from the original text. This approach is straightforward and preserves the meaning of the text well. Algorithms used in extractive summarization include:

• Scoring-Based: Assigns scores to each sentence, and sentences with high scores are included in the summary.
• Graph-Based (TextRank): Represents sentences as nodes in a graph and uses algorithms similar to PageRank to extract key sentences.

2. Methods of Extractive Summarization

The following are common methods of extractive summarization:

Scoring-Based Summarization

In scoring-based summarization, each sentence is assigned an importance score based on features such as:

• TF-IDF: Evaluates the importance of words in the text and uses it to score sentences.
• Sentence Position: Sentences appearing at the beginning or end of a document may receive higher scores if deemed important.

Graph-Based Summarization (TextRank)

TextRank models the document as a graph where each sentence is a node, and the similarity between sentences is represented as the weight of the edges connecting them. By applying a PageRank-like algorithm, important sentences are extracted.

Abstractive Summarization

1. Basics of Abstractive Summarization

Abstractive Summarization generates new sentences based on the content of the original text. Unlike extractive summarization, the summarized text often does not contain phrases directly from the original text, allowing for more human-like expression.

2. Methods of Abstractive Summarization

Abstractive summarization typically uses Seq2Seq models and attention mechanisms. Recently, large pre-trained language models like BERT and GPT have become prominent.

Abstractive Summarization with Seq2Seq Models

Seq2Seq Models are commonly used in abstractive summarization. These models have an encoder-decoder structure where the encoder understands the original text, and the decoder generates a new summary.

Abstractive Summarization with Transformer Models

Transformer models leverage the Attention mechanism to capture context effectively and perform well in abstractive summarization. Notably, using pre-trained models like BERT and GPT enables more natural summarization.

Implementation Examples of Summarization

1. Extractive Summarization Using Gensim

Here, we demonstrate how to implement extractive summarization using the Python library Gensim.

Installing Necessary Libraries

First, install the Gensim library:

pip install gensim

Extractive Summarization with Gensim

Using Gensim, we can perform extractive summarization with the TextRank algorithm:

from gensim.summarization import summarize

# Sample text
text = """
Machine learning is a field of artificial intelligence (AI) that uses statistical techniques to give computer systems 
the ability to learn from data, without being explicitly programmed. The name machine learning was coined in 1959 by 
Arthur Samuel. Machine learning is closely related to computational statistics, which focuses on making predictions using computers.
"""

# Summarizing the text using Gensim
summary = summarize(text, ratio=0.5)  # Summarize 50% of the text
print("Original Text:")
print(text)
print("\nSummary:")
print(summary)

This code summarizes 50% of the original text using Gensim’s summarize function, providing a simple and quick way to create summaries.

2. Abstractive Summarization Using Transformers

Next, we implement abstractive summarization using the Python library transformers and the pre-trained BART model.

Installing the Required Libraries

First, install the transformers library:

pip install transformers

Abstractive Summarization with BART

We use the BART model from the transformers library to summarize a text:

from transformers import pipeline

# Create a summarization pipeline using the BART model
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")

# Sample text
text = """
Machine learning is a field of artificial intelligence (AI) that uses statistical techniques to give computer systems 
the ability to learn from data, without being explicitly programmed. The name machine learning was coined in 1959 by 
Arthur Samuel. Machine learning is closely related to computational statistics, which focuses on making predictions using computers.
"""

# Summarize the text using BART
summary = summarizer(text, max_length=50, min_length=25, do_sample=False)
print("Original Text:")
print(text)
print("\nSummary:")
print(summary[0]['summary_text'])

This code uses BART to perform abstractive summarization. By adjusting parameters like max_length and min_length, the summary length can be controlled.

Evaluation Methods for Text Summarization

1. ROUGE (Recall-Oriented Understudy for Gisting Evaluation)

ROUGE measures the overlap between the generated summary and reference summaries, especially n-gram overlaps. ROUGE-N and ROUGE-L are commonly used to evaluate text summaries.

2. BLEU (Bilingual Evaluation Understudy)

BLEU, typically used for translation tasks, can also evaluate abstractive summarization by comparing the generated summary with the reference summary based on n-gram matches.

Summary

In this episode, we covered the basics of text summarization, discussing both extractive and abstractive approaches. Text summarization is a crucial technology for efficient information dissemination and is widely used in news summarization and report generation.

Next Episode Preview

Next time, we will explain language model evaluation methods, including metrics like perplexity, to understand how to measure model performance.

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Notes

1. Extractive Summarization: A method that directly extracts parts of the original text to create a summary.
2. Abstractive Summarization: A method that generates new sentences based on the content of the original text for summarization.
3. ROUGE Score: A recall-oriented metric for evaluating summaries.