How does Spanish Text Summarizer work?
- Extractive Summarization. This method identifies and extracts the most important sentences from a text based on their relevance and significance.
- Abstractive Summarization. This technique generates new sentences that convey the core ideas of the text, allowing for more fluent and natural summaries.
- Machine Learning. Machine learning algorithms are used to train the summarizer to better understand language patterns, improving the quality of the summaries over time.
- Natural Language Processing (NLP). NLP techniques are employed to analyze and comprehend the structure and semantics of the Spanish language, aiding in more accurate summarization.
