How does Hebrew Text Summarizer work?
- Tokenization. Tokenization breaks down the text into individual words or phrases, allowing for better analysis of the content structure.
- Text Ranking. This technique scores sentences based on their importance and relevance to the main topic, ensuring the most valuable information is highlighted.
- Natural Language Processing (NLP). NLP techniques are employed to understand the context and semantics of the text, which aids in producing coherent summaries.
- Machine Learning. Machine learning algorithms are used to adapt and improve summarization quality over time based on user interactions and feedback.