Natural Language Processing (NLP) is a field that develops methods for computers to understand and process human language. Among these, Text Summarization is a technology that extracts important information from lengthy documents or articles and condenses it into a summary. It has gained particular attention due to recent advancements in deep learning. This article will detail the principles, methodologies, and application areas of deep learning-based text summarization.
1. Definition and Necessity of Text Summarization
Text summarization is the process of extracting key information from a given document or summarizing the content to make it easier to read. With the vast amount of information in modern society, the necessity for text summarization technology has become even more emphasized. In an age of information overload, the need for technologies that save time and resources while grasping important content has become urgent.
1.1 Types of Text Summarization
Text summarization is broadly divided into two types.
- Extractive Summarization: This method summarizes by selecting important sentences or words from the document. Since it uses existing text, the context remains natural.
- Abstractive Summarization: This method reconstructs the content of the document to produce new sentences. It is more creative and provides a summary that expresses the information differently from the original.
2. Advancement of Deep Learning and Text Summarization
Deep learning is a type of artificial neural network that exhibits powerful performance in learning patterns from large-scale data. Notably, the performance improvement in the field of natural language processing is remarkable due to its ability to learn and understand more complex language models than commonly used machine learning algorithms.
2.1 Recurrent Neural Networks (RNN) and LSTM
Early NLP models used Recurrent Neural Network (RNN) architectures, but RNNs have the disadvantage of suffering from long-term dependency issues with long sequence data. To address this, structures like Long Short-Term Memory (LSTM) were developed. LSTMs effectively handle long-range dependencies by regulating the processes of storing and forgetting information through memory cells and gate structures.
2.2 Transformer Model
Introduced in 2020, the Transformer architecture brought innovation to the field of NLP. The Transformer model effectively captures the relationships between each word in a document through a Self-Attention mechanism. This structure is advantageous for parallel processing, resulting in faster speeds and excellent performance on large datasets.