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This technique attempts to understand the underlying semantics of the text using language models and ontologies. Example: Summarizing a medical article by understanding and relating complex medical terms.

Assumes the most important information is present at the start of the text. Common in news articles. Example: Automatically extracting the first few sentences of a news article.

An extractive and unsupervised algorithm inspired by Google's PageRank. It uses a graph-based ranking model for sentences. Example: A summarization tool using TextRank to identify key sentences in a lengthy article.

This technique involves selecting a subset of words that retain the most important points of the original text. The sentences are chosen based on a ranking algorithm. Example: A news aggregator providing headlines and key points.

Leverages supervised or unsupervised machine learning models to learn how to summarize text. Example: A system trained on a corpus of documents and summaries to learn summarization patterns.

Focuses on identifying the key topics within the text and creating a summary around them. Example: Summarizing research papers by highlighting the main topics discussed.

Combines both extractive and abstractive methods to take advantage of the strengths of both. Example: A summarization tool that extracts sentences and then paraphrases for conciseness and fluency.

Abstractive summarization generates new sentences and phrases to condense the original text while capturing its meaning. Example: A machine-generated summary that rephrases and condenses a longer document.

Employs deep learning models like RNNs, LSTMs, and Transformers to generate summaries. Example: Using a fine-tuned BERT model to produce summaries that capture nuances.