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Related Concept Videos

Transformers01:26

Transformers

1.7K
A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
1.7K
Source Transformation01:15

Source Transformation

11.0K
Source transformation is a fundamental technique employed in circuit analysis, offering a valuable tool for simplifying complex electrical circuits. This technique involves the replacement of either a voltage source in series with a resistor by a current source in parallel with a resistor, or vice versa. The key concept here is that when the original sources are deactivated (turned off), the equivalent resistance at the circuit's end terminals remains the same.
It is essential to note that when...
11.0K
Types Of Transformers01:16

Types Of Transformers

1.4K
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
1.4K
SBAR II: Application of SBAR01:14

SBAR II: Application of SBAR

5.6K
SBAR is an effective communication tool used by healthcare professionals to communicate patient information accurately. SBAR stands for Situation, Background, Assessment, and Recommendation. For a better understanding, an example is given below.
SBAR Report from a Nurse to a Health Care Provider
S: "Hello, Dr. Smith. This is Jane, RN, from the Med Surg unit. I am calling to tell you about Ms. White in Room 210, who is experiencing increased pain and redness at her incision site. Her recent...
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Techniques of Therapeutic Communication II: Focusing, Paraphrasing, and Summarizing01:23

Techniques of Therapeutic Communication II: Focusing, Paraphrasing, and Summarizing

10.8K
Focusing involves centering a conversation on a message's critical elements or concepts. Focusing is valuable if the talk is vague or patients begin to repeat themselves. Sometimes, when patients are asked about their symptoms, they may go off-topic and try to tell their entire life story. Respectfully, the nurse should bring the conversation back into focus.
This therapeutic technique can also be used when a patient brings up pertinent information during a health-related conversation. The...
10.8K
Energy Losses in Transformers01:21

Energy Losses in Transformers

1.3K
In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the...
1.3K

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Related Experiment Video

Updated: Jan 8, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

986

Text summarization method of argumentative discourse by combining the BERT-transformer model.

Yaser Altameemi1, Mohammed Altamimi2, Adel Alkhalil3

  • 1Department of English, College of Arts and Literature, University of Ha'il, Ha'il, Saudi Arabia.

Frontiers in Artificial Intelligence
|December 15, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel extractive-abstractive text summarization method, combining Bidirectional Encoder Representations from Transformers (BERT) and transfer learning to effectively summarize complex argumentative texts.

Keywords:
abstractive text summarizationargumentative discoursebidirectional encoder representations from transformers (BERT)extractive text summarizationtransformer model

Related Experiment Videos

Last Updated: Jan 8, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

986

Area of Science:

  • Natural Language Processing
  • Artificial Intelligence

Background:

  • Text summarization is crucial for distilling main ideas.
  • Extractive and abstractive methods have limitations in capturing complete information.
  • Combining techniques can enhance summarization performance and quality.

Purpose of the Study:

  • To propose a novel methodology for summarizing complex texts, specifically argumentative discourse.
  • To improve summarization performance and summary generation quality by integrating extractive and abstractive techniques.

Main Methods:

  • Developed a hybrid extractive-abstractive text summarization approach.
  • Utilized Bidirectional Encoder Representations from Transformers (BERT) and transfer learning.
  • Applied the method to a dataset of UK parliamentary debates.

Main Results:

  • The proposed method effectively summarizes the main points of complex texts.
  • Achieved ROUGE-1 scores of 30.1 and 36.2, ROUGE-2 scores of 9.60 and 11.80, and ROUGE-L scores of 27.9 and 31.5 for two debates.
  • Demonstrated superior performance compared to standalone extractive or abstractive methods.

Conclusions:

  • The novel extractive-abstractive method enhances text summarization by leveraging the strengths of both approaches.
  • This hybrid technique ensures comprehensive coverage of main points in complex documents.
  • The approach shows significant potential for improving the efficiency and quality of automated text summarization.