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Posted: 10 Feb 2022 03:00

“Extractive Summarization” February 2022 — summary from Crossref

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“Extractive Summarization” February 2022 — summary from Crossref 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 has a crucial duty in natural language processing. Research on text summarization in Indonesian Language is still uncommon and not evaluated adequately. Automatic Text Summarization is definitely valuable because of a vast quantity of textual data and lengthy manual summarization. In order to improve ATS for solitary documents in huge datasets, a new extractive graph framework-text extractive SUMmarization structure based on EDge info with COreference resolution EDCOSUM is proposed in this paper that counts on coreference resolution, including side details in word-level graph and a sentence-ranking technique. Together with the boosting number of clinical magazines, many scientific communities must review the entire text to get the significance of details from a journal post. The purpose of this research is to develop an extractive text summarization by doing attribute engineering to remove the semantic info from the initial text. The task of summarization can be categorized right into two methods, abstractive and extractive. Extractive summarization selects the significant sentences from the original document to develop a summary, while abstractive summarization interprets the original document and generates the recap in its own words. Presently, the graph model-based summary model has issues such as insufficient semantic blend in between nodes and lack of location info. This paper suggests a single-document extraction text summary model based on a heterogeneous graph interest neural network, utilizing HGT, Heterogeneous Graph Attention Neural Network to address the defect of not enough deep semantic blend of nodes, and make use of trainable setting coding to solve the problem of missing position details.



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


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

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