Tekstikokkuvõtteid

Tekstikokkuvõtteid, commonly known as text summarization, is the process of creating a shorter version of a longer text that retains the most important information. This is achieved through either extractive or abstractive methods. Extractive summarization involves selecting key sentences or phrases directly from the original text and stitching them together to form a summary. Abstractive summarization, on the other hand, aims to understand the meaning of the source text and then generate new sentences that convey the core ideas in a more concise way, much like a human would.

The purpose of text summarization is to help users quickly grasp the main points of a document without having to read the entire piece. This is particularly useful for dealing with large volumes of information, such as news articles, research papers, or long reports. Automated text summarization systems leverage natural language processing (NLP) techniques to identify significant content. These techniques can involve analyzing sentence structure, word frequency, and the semantic relationships between different parts of the text.

Applications of text summarization are widespread. They are found in search engines to provide snippets of web pages, in news aggregators to offer brief overviews of articles, and in academic tools to help researchers quickly assess the relevance of papers. The development of more sophisticated AI models has led to increasingly accurate and coherent abstractive summaries, bridging the gap between machine-generated and human-written summaries.