Summarizer - is a good long term project for the future

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It is a completely computerized newspaper.

Summarizer  uses artificial intelligence to make your daily messages shorter. Its bots search the internet for messages, summarize them, and then categorize them.

What exactly is a SUMMARIZER?

Summarizer is only available to $ SMR owners. You don't have to spend anything to read the contents of Summarizer; All you have to do is hold $ SMR. You can always sell your $ SMR back to the market if you choose to stop reading Summarizer.

Our algorithm is as follows:

TextRank is an unattended method of automatic text summarization that can also be used to extract key keywords from a document. The method uses a variant of PageRank over a graph specially created for the summary. This leads to an assessment of the elements of the diagram, with the most important elements being those that best describe the text. This method allows TextRank to produce summaries without the use of a training corpus or label, and also allows the algorithm to work with a wide variety of languages.

TextRank models each document as a network with sentences as nodes for automatic summarization. In order to create edges between sentences, a function for calculating sentence similarity is required. This function is often used to weight the edges of the graph; the greater the similarity between words, the more significant the graph edge connecting them. We can argue that if they are extremely similar in the realm of a random walker commonly used in PageRank, they are more likely to switch from one sentence to another.

Based on the material both phrases contain, TextRank estimates the degree of similarity between them. To avoid elevating long sentences, this overlap is simply calculated as the number of similar lexical tokens among them divided by the length of each phrase.

The function of the original algorithm can be formalized as follows:

Definition 1: Given Si, Sj, two sentences are represented by a collection of n words expressed in Si as Si = wi, wi, ..., wi. For Si, Sj the similarity function is as follows:

As a result of this procedure, the document is displayed as a thick graph. PageRank is used to calculate the significance of each vertex on this chart. As a summary, the most important sentences are selected and given in the same order as they appear in the document. These concepts are based on changing the way the distances between phrases are calculated to weight the vertices of the PageRank graph. Since these similarity metrics are not related to the TextRank model, they can easily be integrated into the algorithm. We found that some of these optimizations resulted in significant improvements over the original algorithm.

The BM25 ranking function (also known as Okapi-BM25) is a state-of-the-art ranking function for information gathering tasks. The BM25 model is a probabilistic version of the TF-IDF model.

To learn more, visit:

Website:  https://summarizer.co
Facebook:  https://www.facebook.com/Summarizer-100158469034381
Twitter:  https://twitter.com/SummarizerC
Telegramm:  https://t.me/SummarizerOffizielles
Medium:  https: //medium.com/@summarizer
Reddit:  https://www.reddit.com/user/Summarizer_Official
IDO:  https://token.summarizer.co/#whitelist

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