Artificial Intelligence Task

Summarization

The ability of processing great volumes of textual corpus and to shorten it while extracting the most relevant information is one of the most promising NLP fields. Summarization models can use a variety of techniques, such as natural language processing and machine learning, to identify key information and generate a condensed version of the original text. While still a developing field, AI summarization has the potential to greatly improve productivity and accessibility in various industries.

Input

Long text documents such as reports, articles, legislation or technical papers

Output

A summary of the corpus with a variable length

Goal

To distill the essential information from a lengthy document

Learning Strategy

Extractive or abstractive techniques by a long context LLM or embeddings

Evaluation Metric

Coherence, coverage, informational density, brevity, and readability

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