Update files from the datasets library (from 1.2.1)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.1

README.md

@@ -46,23 +46,22 @@ task_ids:
 
 ## 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
+- **Homepage:** [XSUM Hallucination Annotations Homepage](https://research.google/tools/datasets/xsum-hallucination-annotations/)
+- **Repository:** [XSUM Hallucination Annotations Homepage](https://github.com/google-research-datasets/xsum_hallucination_annotations)
+- **Paper:** [ACL Web](https://www.aclweb.org/anthology/2020.acl-main.173.pdf)
 - **Point of Contact:** [xsum-hallucinations-acl20@google.com](mailto:xsum-hallucinations-acl20@google.com)
 
 ### 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.
+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. This dataset contains a large scale human evaluation of several neural abstractive summarization systems to better understand the types of hallucinations they produce. 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]
+* `summarization`: : The dataset can be used to train a model for Summarization,, which consists in summarizing a given document. Success on this task is typically measured by achieving a *high/low* [ROUGE Score](https://huggingface.co/metrics/rouge).
 
 ### Languages
 
-[More Information Needed]
+The text in the dataset is in English which are abstractive summaries for the [XSum dataset](https://www.aclweb.org/anthology/D18-1206.pdf). The associated BCP-47 code is `en`.
 
 ## Dataset Structure
 
@@ -70,6 +69,10 @@ Neural abstractive summarization models are highly prone to hallucinate content
 
 ##### Faithfulness annotations dataset
 
+A typical data point consists of an ID referring to the news article(complete document), summary, and the hallucination span information.
+
+An example from the XSum Faithfulness dataset looks as follows:
+
 ```
 {
 'bbcid': 34687720,
@@ -84,6 +87,10 @@ Neural abstractive summarization models are highly prone to hallucinate content
 
 ##### Factuality annotations dataset
 
+A typical data point consists of an ID referring to the news article(complete document), summary, and whether the summary is factual or not.
+
+An example from the XSum Factuality dataset looks as follows:
+
 ```
 {
 'bbcid': 29911712,
@@ -101,14 +108,14 @@ Neural abstractive summarization models are highly prone to hallucinate content
 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'
+- `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`: Worker ID (one of 'wid_0', 'wid_1', 'wid_2')
 
 
 The `hallucination_type` column has NULL value for some entries which have been replaced iwth `-1`.
@@ -118,18 +125,23 @@ The `hallucination_type` column has NULL value for some entries which have been
 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'
+- `bbcid1: 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`: Worker ID (one of '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]
+There is only a single split for both the Faithfulness annotations dataset and Factuality annotations dataset.
+
+|                          | Tain  |
+| ------------------------ | ----- |
+| Faithfulness annotations | 11185 |
+| Factuality annotations   | 5597  |
 
 ## Dataset Creation
 
@@ -183,8 +195,18 @@ The `is_factual` column has NULL value for some entries which have been replaced
 
 ### Licensing Information
 
-[More Information Needed]
+[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode)
 
 ### Citation Information
 
-[More Information Needed]
+```
+@InProceedings{maynez_acl20,
+  author =      "Joshua Maynez and Shashi Narayan and Bernd Bohnet and Ryan Thomas Mcdonald",
+  title =       "On Faithfulness and Factuality in Abstractive Summarization",
+  booktitle =   "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
+  year =        "2020",
+  pages = "1906--1919",
+  address = "Online",
+}
+```
+