
# SummEval
The annotations include summaries generated by 16 models from 100 source news articles (1600 examples in total).
Each of the summaries was annotated by 5 indepedent crowdsource workers and 3 independent experts (8 annotations in total).
Summaries were evaluated across 4 dimensions: coherence, consistency, fluency, relevance.
Each source news article comes with the original reference from the CNN/DailyMail dataset and 10 additional crowdsources reference summaries.

For this dataset, we averaged the 3 **expert** annotations to get the human scores.

source: https://github.com/Yale-LILY/SummEval
language:en region:us
{"language": ["en"]}
TAGS
#language-English #region-us 


# SummEval
The annotations include summaries generated by 16 models from 100 source news articles (1600 examples in total).
Each of the summaries was annotated by 5 indepedent crowdsource workers and 3 independent experts (8 annotations in total).
Summaries were evaluated across 4 dimensions: coherence, consistency, fluency, relevance.
Each source news article comes with the original reference from the CNN/DailyMail dataset and 10 additional crowdsources reference summaries.

For this dataset, we averaged the 3 expert annotations to get the human scores.

source: URL
# SummEval
The annotations include summaries generated by 16 models from 100 source news articles (1600 examples in total).
Each of the summaries was annotated by 5 indepedent crowdsource workers and 3 independent experts (8 annotations in total).
Summaries were evaluated across 4 dimensions: coherence, consistency, fluency, relevance.
Each source news article comes with the original reference from the CNN/DailyMail dataset and 10 additional crowdsources reference summaries.

For this dataset, we averaged the 3 expert annotations to get the human scores.

source: URL
TAGS
#language-English #region-us 
 # SummEval
The annotations include summaries generated by 16 models from 100 source news articles (1600 examples in total).
Each of the summaries was annotated by 5 indepedent crowdsource workers and 3 independent experts (8 annotations in total).
Summaries were evaluated across 4 dimensions: coherence, consistency, fluency, relevance.
Each source news article comes with the original reference from the CNN/DailyMail dataset and 10 additional crowdsources reference summaries.

For this dataset, we averaged the 3 expert annotations to get the human scores.

source: URL
passage: TAGS
#language-English #region-us 
# SummEval
The annotations include summaries generated by 16 models from 100 source news articles (1600 examples in total).
Each of the summaries was annotated by 5 indepedent crowdsource workers and 3 independent experts (8 annotations in total).
Summaries were evaluated across 4 dimensions: coherence, consistency, fluency, relevance.
Each source news article comes with the original reference from the CNN/DailyMail dataset and 10 additional crowdsources reference summaries.

For this dataset, we averaged the 3 expert annotations to get the human scores.

source: URL