Business performance assistant
The content below is machine-generated by Brevi Technologies’ NLG model, and the source content was collected from open-source databases/integrate APIs.
Text summarization has a crucial duty in all-natural language processing. Research on text summarization in Indonesian Language is still unusual and not examined adequately. The purpose of this research study is to provide an automatic text summarization experience making use of the extractive technique within an application implemented on Android. The code application utilizing Flutter Software Development Kit on client-side and Python with Flask on server-side is intended to have better combination and upkeep procedure.
Currently, mostly all enterprises are oriented into structuring text data in abundance, savoring the benefits of big information principle yet the truth is that it's not virtually possible to undergo all this data/documents for decision production due to the fact that at the moment, restraint. By adopting these summing up strategies, the precision in information access of summed up content through search questions can be improved compared to carrying out search over a wide series of initial textual content.
This paper provides a query-based extractive text summarization approach by utilizing sense-oriented semantic relatedness action. We also observe that our query-based extractive text summarization approach can identify query relevance sentences which meet the question need.
This can serve as an example of how to use Brevi Assistant and integrated APIs to analyze text content.
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