Summarization

Automatically generate an abstract with most relevant concepts.
Summarize extractively or abstractively.
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Automatic summarization

is the process whereby a text (page, paragraph, full document) is shortened and another document is created using machine learning software, in order to create an abstractive or extractive summary with the most relevant points of the original document.

Extract knowledge documents quickly and reduce manual processes

Pangea’s summarization tools can mine the most important elements in a document using state-of-the-art mathematical calculations to ascertain the most relevant parts of a document.

Extractive Summarization

Anonymization available in 25 languages, machine translation in more than 500 language combinations.

Abstractive Summarization

Abstractive summarization offers a short abstract of the text, possibly using words not included in the original. Our system is trained on heuristic approaches to understand the whole context and generate a summary based on that understanding.
PangeaMT has developed summarization services technologies that can make a coherent summary of any text taking into account variables such as length, syntax, facts, figures, people as well as writing style, for example

Automatic data summarization is part of machine learning and data mining. The main goal of summarization is to find a subset of data which contains the “information” of the entire set. Similar techniques are widely used in industry today. Search engines are an example. Document summarization aims at creating an abstract or representative summary of the entire document, by finding the most informative sentences. Similar techniques can be utilized in image summarization by a software system to find the most representative and important images.

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