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Posted: 24 Nov 2021 05:00

“Document Summarization” November 2021 — summary from Crossref

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“Document Summarization” November 2021 — 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


In this research study, the extractive summarization making use of sentence embeddings created by the finetuned BERT models and the K-Means clustering method has been investigated. To demonstrate how the BERT model can catch the expertise in certain domains, like engineering design and what it can create after being finetuned based on domain-specific datasets, a number of BERT models are trained, and the sentence embeddings extracted from the finetuned models are made use of to generate summaries of a set of documents.

The advancement of modern technologies has created a huge quantity of data over the internet. Social media internet sites are playing a major function in publishing information occasions on similar topics with various components. Named entity recognition is a subject of the details extraction. It is a process in which the text document with the assistance of software will produce a summary by choosing the important points of the original text.

Abstract in this paper, we use various monitored learning strategies to construct query-focused multi-document summarization systems, where the task is to create automatic recaps in response to an offered inquiry or particular information request mentioned by the customer. Throughout different experiments, we evaluated the influence of automated labeling techniques on the efficiency of the applied supervised methods.

Abstract The synthesis process of document content and its visualization play a fundamental role in the context of knowledge depiction and retrieval. The Visual Semantic Tag Cloud can be utilized not just to manufacture a document yet also to represent a collection of papers organized by classifications using a subject detection strategy based on textual and aesthetic analysis of multimedia functions.


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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