Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Transformers01:26

Transformers

1.1K
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...
1.1K
Types Of Transformers01:16

Types Of Transformers

975
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...
975
Master Transcription Regulators02:23

Master Transcription Regulators

2.2K
2.2K
Energy Losses in Transformers01:21

Energy Losses in Transformers

872
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...
872
Improving Translational Accuracy02:07

Improving Translational Accuracy

2.6K
2.6K
Source Transformation for AC Circuits01:11

Source Transformation for AC Circuits

580
The process of source transformation in the frequency domain entails the conversion of a voltage source, positioned in series with an impedance, into a current source that is parallel to an impedance, or the other way around. It is essential to maintain the following relationships while transitioning from one source type to another.
580
  1. Home
  3. Research Domains
  5. Information And Computing Sciences
  7. Computer Vision And Multimedia Computation
  9. Video Processing
  11. Multimodal Abstractive Summarization Using Bidirectional Encoder Representations From Transformers With Attention Mechanism.

Multimodal Abstractive Summarization using bidirectional encoder representations from transformers with attention mechanism.

Dakshata Argade1, Vaishali Khairnar1, Deepali Vora2

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

Heliyon
|February 29, 2024

Related Experiment Videos

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

555
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

399
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K

View abstract on PubMed

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.

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.
Keywords:
Attention mechanismBidirectional encoder representations from transformerDecoderEncoder

Related Experiment Videos

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

555
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

399
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K
  • 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.
    Multimodal abstractive summarization
    Multimodalities