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MeaningCloud Automatic Summarization API

MeaningCloud Automatic Summarization API
MeaningCloud Automatic Summarization API
0

This API was created by MeaningCloud. The MeaningCloud Automatic Summarization API endpoint is located at https://api.meaningcloud.com/summarization-1.0. You can find the MeaningCloud portal / hompage here. The MeaningCloud Automatic Summarization API is not currently available on the RapidAPI marketplace. Click "Request this API on RapidAPI" to let us know if you would like to access to this API. Meanwhile, you can check out the top APIs that currently available for developers.

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Summarization Summarization
Summarization is MeaningCloud's solution to extract a summary for a given document, selecting the most relevant sentences in it to try to sum up what it is about. This process does not take into account the language when it evaluates the importance of a sentence, which means that it's language independent and can be applied to documents in any language.
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Deep Categorization Deep Categorization
Deep Categorization is MeaningCloud's solution for in-depth rule-based categorization. It assigns one or more categories to a text, using a very detailed rule-based language that allows you to identify very specific scenarios and patterns using a combination of morphological, semantic and text rules.
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Text Clustering Text Clustering
Text Clustering is MeaningCloud's solution for automatic document clustering, i.e., the task of grouping a set of texts in such a way that texts in the same group (called a cluster) are more similar to each other than to those in other clusters.
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Sentiment Analysis Sentiment Analysis
Multilingual sentiment analysis of texts from different sources (blogs, social networks,...). Besides polarity at sentence and global level, Sentiment Analysis uses advanced natural language processing techniques to also detect the polarity associated to both entities and concepts in the text. Sentiment Analysis also gives the user the possibility of detecting the polarity of user-defined entities and concepts, making the service a flexible tool applicable to any kind of scenario. Additionally, Sentiment Analysis detects if the text processed is subjective or objective and if it contains irony marks [beta], both at global and sentence level, giving the user additional information about the reliability of the polarity obtained from the sentiment analysis.
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Text Classification Text Classification
Automatic multilingual text classification according to pre-established categories defined in a model. The algorithm used combines statistic classification with rule-based filtering, which allows to obtain a high degree of precision for very different environments. Three models available: IPTC (International Press Telecommunications Council standard), EuroVocs and Corporate Reputation model. Languages covered are Spanish, English, French, Italian, Portuguese and Catalan.
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Topics Extraction Topics Extraction
Topics Extraction tags locations, people, companies, dates and many other elements appearing in a text written in Spanish, English, French, Italian, Portuguese or Catalan. This detection process is carried out by combining a number of complex natural language processing techniques that allow to obtain morphological, syntactic and semantic analyses of a text and use them to identify different types of significant elements.
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Document Structure Analysis Document Structure Analysis
The Document Structure Analysis extracts different sections of a given document with markup content (which includes formatted documents such as PDF or Microsoft Word files), including the title, headings, abstract and parts of an email. This process, even though it takes into account some language markers, is based mainly in the markup of the document, so it can be applied to documents in any language.
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