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

Lagging Strand Synthesis01:59

Lagging Strand Synthesis

16.0K
16.0K
Lagging Strand Synthesis01:59

Lagging Strand Synthesis

60.6K
During replication, the complementary strands in double-stranded DNA are synthesized at different rates. Replication first begins on the leading strand. Replication starts later, occurs more slowly, and proceeds discontinuously on the lagging strand.
There are several major differences between synthesis of the leading strand and synthesis of the lagging strand. 1) Leading strand synthesis happens in the direction of replication fork opening, whereas lagging strand synthesis happens in the...
60.6K
Additional Subnuclear Structures02:10

Additional Subnuclear Structures

2.4K
2.4K
Graded Potential01:19

Graded Potential

6.5K
Graded potentials are localized fluctuations in the cell membrane's electrical charge, commonly found in the dendrites of neurons. The magnitude of these potential changes depends on the strength of the initiating stimulus. In a membrane at its resting potential, a graded potential signifies a voltage shift either above -70 mV or below -70 mV.
Graded potentials fall into two categories: depolarizing and hyperpolarizing. Depolarizing graded potentials typically occur when sodium (Na+) or...
6.5K
Storage01:23

Storage

296
A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
296
Protein Networks02:26

Protein Networks

2.7K
2.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A Modularized Higher-Order Diagnostic Classification Model for Clustered Attribute Hierarchies.

Multivariate behavioral research·2026
Same author

Uncovering Hierarchical Asymmetries in Artificial Intelligence Transformation: Navigating the Bright and Dark Sides Across Organizational Levels.

Journal of visualized experiments : JoVE·2026
Same author

Effects of divalent cations on diffusion dynamics of biological water confined between lipid membranes.

The Journal of chemical physics·2026
Same author

Neural Network Copulas for Generating Synthetic Test Data Preserving Psychometric Properties.

Journal of Intelligence·2026
Same author

Composite marginal likelihood estimation of higher-order diagnostic classification models under high dimensionality.

The British journal of mathematical and statistical psychology·2026
Same author

A Landmark-Guided Dual-Stream Synergistic Framework for Automated Intracranial Aneurysm Detection in Magnetic Resonance Angiography.

Journal of imaging informatics in medicine·2026

Related Experiment Video

Updated: Dec 30, 2025

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.4K

Abstractive summarization of long texts by representing multiple compositionalities with temporal hierarchical

Dennis Singh Moirangthem1, Minho Lee1

  • 1School of Electronics Engineering, IT-1, Kyungpook National University, 80 Daehakro, Bukgu, Daegu, 41566, South Korea.

Neural Networks : the Official Journal of the International Neural Network Society
|January 17, 2020
PubMed
Summary

This study introduces a temporal hierarchical pointer generator network for abstractive summarization of long texts. The model effectively handles complex structures and improves summary generation accuracy on benchmark datasets.

Keywords:
AbstractionAutomatic summarizationMultiple timescalePointer generatorRNN encoder–decoderSeq2seqTemporal hierarchy

More Related Videos

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.1K

Related Experiment Videos

Last Updated: Dec 30, 2025

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.4K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.1K

Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Machine Learning

Background:

  • Abstractive summarization of long texts presents challenges in capturing multiple compositionalities.
  • Existing models struggle to efficiently represent deep text structures.

Purpose of the Study:

  • To introduce a novel temporal hierarchical pointer generator network.
  • To enhance the representation of multiple compositionalities in long text sequences.
  • To improve abstractive summarization performance for scientific articles and news datasets.

Main Methods:

  • Developed a temporal hierarchical pointer generator network.
  • Utilized a multilayer gated recurrent neural network with an adaptive timescale.
  • Implemented a multiple timescale architecture with learned timescales via backpropagation through time.

Main Results:

  • Successfully implemented a summary generation system for long texts using the multiple timescale with adaptation concept.
  • Demonstrated effective representation of text compositions through the adaptive timescale.
  • Achieved improved performance on the CNN/Daily Mail summarization benchmark dataset.

Conclusions:

  • The proposed temporal hierarchical network effectively handles long text summarization.
  • Adaptive timescales are crucial for representing complex text structures.
  • The model shows promise for advanced natural language processing tasks.