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Posted: 30 Aug 2021 01:00

“Extractive Summarization” August 2021 — summary from DOAJ

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“Extractive Summarization” August 2021 — summary from 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.


The automatic generation of the draft procuratorial pointers is to draw out the summary of unlawful facts, administrative omission, description of laws and laws, and various other info from the case papers. Previously, the existing deep learning techniques mainly concentrate on context-free word embeddings when dealing with legal domain-specific extractive summarization jobs, which can not get a better semantic understanding of the message and in turn brings about an adverse summarization performance. To the most effective of our expertise, this is the first work to use the pretrained language design for extractive summarization jobs in the area of Chinese judicial litigation. Recording and sharing of educational or lecture videos has boosted over the last few years. Therefore, we discover multitudes of lecture videos that include the teacher composing on a surface. In this work, we prolong the AccessMath dataset to produce an unique dataset for benchmarking of lecture video summarization called LectureMath. In this paper, we explain the style and analysis of extractive summarization technique to assist the learners with checking out problems. As existing summarization methods inherently appoint a lot more weights to the essential sentences, our technique predicts the recap sentences that are important as well as legible to the target audience with good accuracy. We used supervised machine learning strategy for recap extraction of science and social topics in the educational text.

 

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

 

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