Abstractive Summarization
Abstractive Summarization attempts to grasp what a text is about and create new sentences that relay that information to the reader. Such summaries rely on complex NLP technologies, such as semantic representation and language modeling and neural network architectures, that allow them to grasp the idea’s essence and generate new and coherent summaries.
Abstractive summarization is capable of generating human-like and informative summaries since it can modify and reorganize the original text, making it shorter and more meaningful. Abstractive summarization is more demanding and depends on computing resources.
Text Summarization Techniques
Despite its manual-to-automated evolution facilitated by AI and ML progress, Text Summarization remains complex. Text Summarization is critical in news, document organization, and web exploration, increasing data usage and bettering decision-making. It enhances the comprehension of crucial information and the value of the text. Combining syntax and semantics, it creates clear, highly coherent summaries, which define people’s connection with information.
In this article, we are going to explore the importance of text summarization and discuss techniques like extractive and abstractive summarization.
Table of Content
- Importance of Text Summarization
- Text Summarization Techniques
- Extractive Summarization
- Techniques used in Extractive Summarization
- 1. Statistical Approaches:
- 2. Graph-Based Methods:
- 3. Machine Learning Algorithms:
- 4. Sentence Scoring:
- Abstractive Summarization
- Techniques Used in Abstractive Summarization
- 1. Sequence-to-Sequence Models:
- 2. Attention Mechanisms:
- 3. Pre-trained Language Models:
- Hybrid Methods
- Conclusion