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ZENODO
Dataset . 2024
License: CC BY NC
Data sources: ZENODO
ZENODO
Dataset . 2024
License: CC BY NC
Data sources: Datacite
ZENODO
Dataset . 2024
License: CC BY NC
Data sources: Datacite
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Abstractive News Captions with High- level cOntext Representation (ANCHOR) dataset

Research datakeyboard_double_arrow_right Dataset 15 Apr 2024Embargo end date: 15 Apr 2024 English Publisher:Zenodo
Authors: Anantha Ramakrishnan, Aashish;

doi: 10.5281/zenodo.10974907 , 10.5281/zenodo.10974908

Abstractive News Captions with High- level cOntext Representation (ANCHOR) dataset

Abstract

The Abstractive News Captions with High-level cOntext Representation (ANCHOR) dataset contains 70K+ samples sourced from 5 different news media organizations. This dataset can be utilized for Vision & Language tasks such as Text-to-Image Generation, Image Caption Generation, etc.

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Keywords

Artificial intelligence, Natural language processing, Computer vision

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    popularity
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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