↓ Skip to main content

QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension

Overview of attention for article published in ACM Computing Surveys, February 2023
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#16 of 1,418)
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

blogs
1 blog
twitter
131 X users
facebook
1 Facebook page

Citations

dimensions_citation
44 Dimensions

Readers on

mendeley
145 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension
Published in
ACM Computing Surveys, February 2023
DOI 10.1145/3560260
Authors

Anna Rogers, Matt Gardner, Isabelle Augenstein

X Demographics

X Demographics

The data shown below were collected from the profiles of 131 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 145 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 145 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 25 17%
Researcher 11 8%
Student > Master 10 7%
Other 4 3%
Student > Bachelor 3 2%
Other 15 10%
Unknown 77 53%
Readers by discipline Count As %
Computer Science 48 33%
Linguistics 6 4%
Unspecified 2 1%
Business, Management and Accounting 2 1%
Agricultural and Biological Sciences 2 1%
Other 7 5%
Unknown 78 54%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 84. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 December 2022.
All research outputs
#517,656
of 25,784,004 outputs
Outputs from ACM Computing Surveys
#16
of 1,418 outputs
Outputs of similar age
#11,868
of 477,120 outputs
Outputs of similar age from ACM Computing Surveys
#4
of 61 outputs
Altmetric has tracked 25,784,004 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,418 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.8. This one has done particularly well, scoring higher than 98% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 477,120 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 61 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.