Techniques Used in Abstractive Summarization
1. Sequence-to-Sequence Models:
- These are deep learning models that transform an input sequence of text into an output sequence that is the summary.
- Common models include LSTM (Long Short-Term Memory) networks and the more advanced Transformer-based models like BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer).
2. Attention Mechanisms:
- This technique helps the model focus on different parts of the source document dynamically while generating the summary.
- It improves the coherence and relevance of the generated summaries by aligning parts of the input text with the output text.
3. Pre-trained Language Models:
- Models like BERT and GPT can be fine-tuned for specific summarization tasks. They leverage vast amounts of pre-existing text to produce more contextually enriched summaries.
- These models have shown significant promise in generating human-like text.
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