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Posted: 13 Apr 2022 03:00

“Document Summarization” April 2022 — summary from Crossref and DOAJ

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“Document Summarization” April 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

Automatic Text Summarization has been requiring intense research in the last few years. Regardless of the existence of several works, investigates involving the growth of ATS systems for records written in Brazilian Portuguese are still a couple of.

Document indexing is a field of research in Natural Language Processing that has been swiftly advancing for 70 years. The first one concerns the development of a document indexing system making use of the system's operating process based on three phases, specifically pre-processing, weighting, and subject modelling. In this study, the extractive summarization using sentence embeddings produced by the finetuned BERT models and the K-Means clustering technique has been explored. The results show that the BERT model finetuned with a bigger dataset can generate summaries with more domain terms than the pretrained BERT model.

Automatic text summarization essences information from a source text and offers it to the individual in a condensed kind while maintaining its primary content. Analysis results on Malayalam information short articles reveal that the summary generated by the proposed method is more detailed to the human-generated summaries than the existing text summarization methods. Text Summarization is the strategy in which the source document is streamlined, beneficial info is distilled and a concise version is produced. In this paper, a thorough comparison of the several multi-document text summarization techniques such as Machine Learning based, Game-Theory based, Graph based and much more has been presented.

Source texts:

DOAJ - summary generated by Brevi Assistant

Extractive summarization aims to produce a concise version of a document by removing information-rich sentences from the original messages. The graph-based model is an effective and efficient approach to placing sentences since it is straightforward and easy to make use of.

In today's scenario, the rate of development of details is increasing greatly on the World Wide Web. The suggested MDSCSA is compared to 2 other nature inspired based summarization strategies such as Particle Swarm Optimization based summarization and Cat Swarm Optimization based summarization.

Under the restriction of memory capability of the neural network and the document size, it is difficult to generate summaries with adequate salient information. The refined self-matching mechanism not just develops global document interest yet regards association with neighboring signals. Lately, neural sequence-to-sequence models have made remarkable development in abstractive document summarization. First, we present a diversity-promoting beam search strategy in the decoding process, which minimizes the serious diversity issue triggered by basic light beam search and therefore increases the possibility of producing summary sequences that are a lot more interesting.

Automatic document summarization is an area of all-natural language processing that is rapidly boosting with the growth of end-to-end deep learning models. The second is a word association technique to update the information of each word by comparing the details of the present step with the information of all previous translating actions.

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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