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Posted: 07 May 2022 04:00

“Text Summarization” May 2022 — summary from Crossref and DOAJ

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“Text Summarization” May 2022 — summary from Crossref and DOAJ main image

The content below is machine-generated by Brevi Technologies’ NLG model, and the source content was collected from open-source databases/integrate APIs.


Crossref - summary generated by Brevi Assistant


Text summarization is a process for developing a succinct version of a document preserving its main content. With an examination on benchmark DUC2001 and DUC2002 data sets, the ROUGE worth of summaries got by the suggested approach showed its legitimacy, compared to the traditional approaches of sentence selection and the leading three doing systems for DUC2001 and DUC2002.

Automatic text document summarization is an active research location in the text mining area. In the first proposed model, the authors are utilizing right particular matrix, and the 3rd & 2nd proposed models are based on Shannon degeneration. In this write-up, the writer recommends a new metric of evaluation for automatic summaries of texts. Text exists with a term vector which can be either a word or an expression, with a binary-weighted or incident. Automatic text summarization has recently ended up being an essential tool for decreasing the massive quantity of textual data. The QGA is utilized inside a totally automated system as an optimizer to look for the finest combination of sentences to be put in the final summary.

As long as the web customer is increasing, online electronic content is growing proportionally regardless of languages. A lot of research deals with English text summarization has emerged to deal with this massive body of on-line text.


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DOAJ - summary generated by Brevi Assistant


The explosion of online and offline data has changed exactly how we gather, examine, and understand data. Arabic is a widely talked language that is regularly utilized for content sharing on the web, Arabic text summarization of Arabic content is still immature and limited because of numerous problems, consisting of the Arabic language morphological framework, the range of languages, and the absence of appropriate information sources. Abstractive text summarization that generates a summary by rewording a long text continues to be an open considerable problem for natural language processing. In this paper, we present an abstractive text summarization model, multi-layered attentional peephole convolutional LSTM that immediately generates a summary from a lengthy text. Given the enhancing variety of papers, sites, on the internet sources, and the users’ desire to promptly access information, automated textual summarization has captured the interest of many scientists in this area. Scientists have offered various methods for text summarization along with a helpful summary of those texts including relevant document sentences.

Text summarization is a procedure of distilling the most vital content from text records. The discovered lessons are gone over and the opportunities for using the theory of Computing with Words in text summarization are elaborated. Automatic text summarization intends to generate summaries for one or even more messages using machine techniques. Second, we propose a novel sentence extraction algorithm which selects sentences with leading ranked terms and maximum diversity.


This can serve as an example of how to use Brevi Assistant and integrated APIs to analyze text content.


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