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Posted: 15 Dec 2021 01:00

“Text Summarization” December 2021 — summary from Crossref and Springer Nature

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“Text Summarization” December 2021 — summary from Crossref and Springer Nature 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

Automatic text summarization aims at condensing the materials of a text into a basic and detailed summary. In this work, we investigate whether the combination of recaps extracted from multiple areas of long scientific texts may boost the quality of the recap for the whole document.

Text summarization has an essential role in natural language processing. Research on text summarization in Indonesian Language is still unusual and not examined adequately. Attentional sequence-to-sequence models based upon RNN have attained promising efficiencies in the automatic abstractive summarization of modern technology. The speculative results on the LCSTS dataset reveal that the here and now model can properly enhance the ROUGE scores and can better sum up the resource document while maintaining the crucial information.

With the rapid advancement of the global economic situation, natural disasters and emergency situations regularly happen. When a different social emergency occurrence occurs, a large quantity of calamity mishap information and emergency situation case managing steps on the Internet can be used to offer technical reference and support decision-making. Because of the problems of exposure bias and inadequate comment data in the existing Chinese short text summary algorithms, a new pre-training brief text recap algorithm named ERNIE-GEN-CTS based upon ERNIE-GEN is suggested. This paper uses a pre-training language model to recognize the double interest of word degree and word degree for short text, and fills up the below block with semantic details to enhance the exposure predisposition problem.

Source texts:

Springer Nature - summary generated by Brevi Assistant

With the continuous boost in the amount of available information, especially textual, there exists a need for an automated and efficient text summarization mechanism. 2 major text summarization formulas have been the focus of countless research studies: abstractive and extractive summarization. The most recent and accurate info relating to the biomedical and health care domain is called for in the current pandemic situation. Rouge-1 and Rouge-L ratings are empirically determined, offering a comparison between the ordinary F-score, precision, and recall values for different graph-based sentence removal approaches. There is a significant quantity of data which exists online, and to extract the helpful content is a challenging job. In this paper, an ensembled method for text summarization is recommended, in which the effectiveness of extractive summarization and abstractive summarization is incorporated to make one of the most sense out of the raw information.

Because of the rising use of the Internet and other on-line resources, there is tremendous development in the data of text records. This paper presents a title forecast model for research papers making use of the Recursive Recurrent Neural Network and assesses its efficiency by comparing it with sequence-to-sequence models. Human beings make use of speech as a standard type of interaction, extending this idea to the globe of computers will develop a landmark in the field of modern technology.

This paper describes the technique of utilizing a speech recognition system and text summarization model for a professor or educator by videotaping the lecture provided during the class and passing the tape-recorded lecture to the text summarization model.

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

Source texts:


The Brevi assistant is a novel way to summarize, assemble, and consolidate multiple text documents/contents.


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