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Transformers

Transformers

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.
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Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
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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.
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Related Experiment Video

Updated: Jul 2, 2025

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

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

Published on: December 6, 2024

Multimodal Abstractive Summarization using bidirectional encoder representations from transformers with attention

Dakshata Argade¹, Vaishali Khairnar¹, Deepali Vora²

¹Terna Engineering College, Nerul, Navi Mumbai, 400706, India.

|February 29, 2024

Summary

This summary is machine-generated.

This study introduces Multimodal Abstractive Summarization using Bidirectional Encoder Representations from Transformers (MAS-BERT) for summarizing lengthy videos. MAS-BERT significantly improves summarization accuracy, outperforming existing models for better video search and user experience.

Keywords:

Attention mechanism Bidirectional encoder representations from transformer Decoder Encoder Multimodal abstractive summarization Multimodalities

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Area of Science:

Natural Language Processing
Computer Vision
Artificial Intelligence

Background:

Multimodal abstractive summarization aims to create concise summaries from diverse information sources.
Existing methods struggle with lengthy videos, yielding suboptimal summarization results.
Efficient video search is crucial for users to quickly assess video relevance.

Purpose of the Study:

To develop an advanced multimodal abstractive summarization technique for lengthy videos.
To enhance video searchability and user experience on video-sharing platforms.

Main Methods:

Proposed Multimodal Abstractive Summarization using Bidirectional Encoder Representations from Transformers (MAS-BERT) with an attention mechanism.
Utilized Bidirectional Gated Recurrent Unit (Bi-GRU) and Long Short Term Memory (LSTM) encoders for data encoding.
Employed a BERT-based attention mechanism for modality fusion and a Bi-GRU decoder for summary generation.

Main Results:

MAS-BERT achieved a Rouge-1 score of 60.2, outperforming existing models like D-MmT (49.58) and FLORAL (56.89).
Demonstrated superior performance in abstractive summarization for multimodal, lengthy video content.

Conclusions:

The proposed MAS-BERT model effectively addresses the limitations of current methods for summarizing lengthy videos.
This research offers improved contextual information, enhancing user experience and aiding video platforms in customer retention through better search functionality.