annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
languages:
- en
licenses:
- cc-by-4-0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-xsum
task_categories:
- conditional-text-generation
task_ids:
- summarization
Dataset Card for XSum Hallucination Annotations
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://research.google/tools/datasets/xsum-hallucination-annotations/
- Repository: https://github.com/google-research-datasets/xsum_hallucination_annotations
- Paper: https://www.aclweb.org/anthology/2020.acl-main.173.pdf
- Leaderboard: NA
- Point of Contact: [email protected]
Dataset Summary
Neural abstractive summarization models are highly prone to hallucinate content that is unfaithful to the input document. The popular metric such as ROUGE fails to show the severity of the problem. The dataset consists of faithfulness and factuality annotations of abstractive summaries for the XSum dataset. The dataset has crowdsourced 3 judgements for each of 500 x 5 document-system pairs. This will be a valuable resource to the abstractive summarization community.
Supported Tasks and Leaderboards
[More Information Needed]
Languages
[More Information Needed]
Dataset Structure
Data Instances
Faithfulness annotations dataset
{
'bbcid': 34687720,
'hallucinated_span_end': 114,
'hallucinated_span_start': 1,
'hallucination_type': 1,
'summary': 'rory mcilroy will take a one-shot lead into the final round of the wgc-hsbc champions after carding a three-under',
'system': 'BERTS2S',
'worker_id': 'wid_0'
}
Factuality annotations dataset
{
'bbcid': 29911712,
'is_factual': 0,
'summary': 'more than 50 pupils at a bristol academy have been sent home from school because of a lack of uniform.',
'system': 'BERTS2S',
'worker_id': 'wid_0'
}
Data Fields
Faithfulness annotations dataset
Raters are shown the news article and the system summary, and are tasked with identifying and annotating the spans that aren't supported by the input article. The file contains the following columns:
- bbcid: Document id in the XSum corpus.
- system: Name of neural summarizer.
- summary: Summary generated by ‘system’.
- hallucination_type: Type of hallucination: intrinsic (0) or extrinsic (1)
- hallucinated_span: Hallucinated span in the ‘summary’.
- hallucinated_span_start: Index of the start of the hallucinated span.
- hallucinated_span_end: Index of the end of the hallucinated span.
- worker_id: 'wid_0', 'wid_1', 'wid_2'
The hallucination_type column has NULL value for some entries which have been replaced iwth -1.
Factuality annotations dataset
Raters are shown the news article and the hallucinated system summary, and are tasked with assessing the summary whether it is factual or not. The file contains the following columns:
- bbcid: Document id in the XSum corpus.
- system: Name of neural summarizer.
- summary: Summary generated by ‘system’.
- is_factual: yes (1) or no (0)
- worker_id: 'wid_0', 'wid_1', 'wid_2'
The is_factual column has NULL value for some entries which have been replaced iwth -1.
Data Splits
[More Information Needed]
Dataset Creation
Curation Rationale
[More Information Needed]
Source Data
Initial Data Collection and Normalization
[More Information Needed]
Who are the source language producers?
[More Information Needed]
Annotations
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
[More Information Needed]
Licensing Information
[More Information Needed]
Citation Information
[More Information Needed]