Types of Summarization: Extractive vs. Abstractive
In the realm of text summarization, there are primarily two types that one should be familiar with: Extractive Summarization and Abstractive Summarization. Each serves a different purpose and employs distinct techniques. Understanding these differences is crucial for anyone looking to apply summarization methods effectively.
Extractive Summarization
Definition
Extractive summarization involves selecting and extracting key sentences or phrases directly from the source text to create a summary. This method does not alter the extracted content; instead, it uses existing text to convey the main ideas.How It Works
1. Text Analysis: The algorithm analyzes the text to identify the most important sentences based on various features like frequency of words, sentence length, and position in the text. 2. Scoring System: Sentences are scored, and the highest-scoring sentences are selected for inclusion in the summary. 3. Summary Construction: The selected sentences are combined to form a coherent summary.Example
Consider the following paragraph:> "Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and humans through natural language. The ultimate objective of NLP is to enable computers to understand and process human languages in a valuable way. NLP is used in various applications, including chatbots, translation services, and sentiment analysis."
An extractive summary might look like this:
> "Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and humans through natural language. NLP is used in various applications, including chatbots, translation services, and sentiment analysis."
Abstractive Summarization
Definition
Abstractive summarization, on the other hand, generates a new summary that may contain rephrased or paraphrased content that doesn't necessarily appear in the original text. This method aims to create a more concise and coherent summary by capturing the essence of the content.How It Works
1. Understanding Context: The algorithm interprets the overall context and meaning of the text. 2. Content Generation: New sentences are generated that encapsulate the main ideas, often using techniques from natural language generation. 3. Summary Creation: The newly created sentences are structured to form a summary that is coherent and logical.Example
Using the same source paragraph:An abstractive summary might be:
> "NLP combines AI and linguistics to help machines understand human language, leading to applications like chatbots and translation tools."