Update files from the datasets library (from 1.4.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.4.0

README.md

@@ -21,115 +21,195 @@ task_ids:
 # Dataset Card for CNN Dailymail Dataset
 
 ## Table of Contents
-- [Tasks Supported](#tasks-supported)
-- [Purpose](#purpose)
-- [Languages](#languages)
-- [People Involved](#who-iswas-involved-in-the-dataset-use-and-creation)
-- [Data Characteristics](#data-characteristics)
-- [Dataset Structure](#dataset-structure)
-- [Known Limitations](#known-limitations)
-- [Licensing information](#licensing-information)
+- [Dataset Card for CNN Dailymail Dataset](#dataset-card-for-cnn-dailymail-dataset)
+  - [Table of Contents](#table-of-contents)
+  - [Dataset Description](#dataset-description)
+    - [Dataset Summary](#dataset-summary)
+    - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+    - [Languages](#languages)
+  - [Dataset Structure](#dataset-structure)
+    - [Data Instances](#data-instances)
+    - [Data Fields](#data-fields)
+    - [Data Splits](#data-splits)
+  - [Dataset Creation](#dataset-creation)
+    - [Curation Rationale](#curation-rationale)
+    - [Source Data](#source-data)
+      - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
+      - [Who are the source language producers?](#who-are-the-source-language-producers)
+    - [Annotations](#annotations)
+      - [Annotation process](#annotation-process)
+      - [Who are the annotators?](#who-are-the-annotators)
+    - [Personal and Sensitive Information](#personal-and-sensitive-information)
+  - [Considerations for Using the Data](#considerations-for-using-the-data)
+    - [Social Impact of Dataset](#social-impact-of-dataset)
+    - [Discussion of Biases](#discussion-of-biases)
+    - [Other Known Limitations](#other-known-limitations)
+  - [Additional Information](#additional-information)
+    - [Dataset Curators](#dataset-curators)
+    - [Licensing Information](#licensing-information)
+    - [Citation Information](#citation-information)
+    - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:**
+- **Repository:** [CNN / DailyMail Dataset repository](https://github.com/abisee/cnn-dailymail)
+- **Paper:** [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://papers.nips.cc/paper/5945-teaching-machines-to-read-and-comprehend.pdf), [Get To The Point: Summarization with Pointer-Generator Networks](https://www.aclweb.org/anthology/K16-1028.pdf)
+- **Leaderboard:** [Papers with Code leaderboard for CNN / Dailymail Dataset](https://paperswithcode.com/sota/document-summarization-on-cnn-daily-mail)
+- **Point of Contact:** [Abigail See](mailto:abisee@stanford.edu)
+
+### Dataset Summary
+
+The CNN / DailyMail Dataset is an English-language dataset containing just over 300k unique news aticles as written by journalists at CNN and the Daily Mail. The current version supports both extractive and abstractive summarization, though the original version was created for machine reading and comprehension and abstractive question answering. 
+
+### Supported Tasks and Leaderboards
+
+- 'summarization': [Versions 2.0.0 and 3.0.0 of the CNN / DailyMail Dataset](https://www.aclweb.org/anthology/K16-1028.pdf) can be used to train a model for abstractive and extractive summarization ([Version 1.0.0](https://papers.nips.cc/paper/5945-teaching-machines-to-read-and-comprehend.pdf) was developed for machine reading and comprehension and abstractive question answering). The model performance is measured by how high the output summary's [ROUGE](https://huggingface.co/metrics/rouge) score for a given article is when compared to the highlight as written by the original article author. [Zhong et al (2020)](https://www.aclweb.org/anthology/2020.acl-main.552.pdf) report a ROUGE-1 score of 44.41 when testing a model trained for extractive summarization. See the [Papers With Code leaderboard](https://paperswithcode.com/sota/document-summarization-on-cnn-daily-mail) for more models. 
+
+### Languages
 
-## Tasks supported:
-### Task categorization / tags
+The BCP-47 code for English as generally spoken in the United States is en-US and the BCP-47 code for English as generally spoken in the United Kingdom is en-GB. It is unknown if other varieties of English are represented in the data.
 
-[Versions 2.0.0 and 3.0.0 of the CNN / DailyMail Dataset](https://www.aclweb.org/anthology/K16-1028.pdf) were developed for abstractive and extractive summarization. [Version 1.0.0](https://papers.nips.cc/paper/5945-teaching-machines-to-read-and-comprehend.pdf) was developed for machine reading and comprehension and abstractive question answering. 
+## Dataset Structure
 
-## Purpose
+### Data Instances
 
-Version 1.0.0 aimed to support supervised neural methodologies for machine reading and question answering with a large amount of real natural language training data and released about 313k unique articles and nearly 1M Cloze style questions to go with the articles. Versions 2.0.0 and 3.0.0 changed the structure of the dataset to support summarization rather than question answering. Version 3.0.0 provided a non-anonymized version of the data, whereas both the previous versions were preprocessed to replace named entities with unique identifier labels. 
+For each instance, there is a string for the article, a string for the highlights, and a string for the id. See the [CNN / Daily Mail dataset viewer](https://huggingface.co/datasets/viewer/?dataset=cnn_dailymail&config=3.0.0) to explore more examples.
 
-## Languages 
-### Per language:
+```
+{'id': '0054d6d30dbcad772e20b22771153a2a9cbeaf62',
+ 'article': '(CNN) -- An American woman died aboard a cruise ship that docked at Rio de Janeiro on Tuesday, the same ship on which 86 passengers previously fell ill, according to the state-run Brazilian news agency, Agencia Brasil. The American tourist died aboard the MS Veendam, owned by cruise operator Holland America. Federal Police told Agencia Brasil that forensic doctors were investigating her death. The ship's doctors told police that the woman was elderly and suffered from diabetes and hypertension, according the agency. The other passengers came down with diarrhea prior to her death during an earlier part of the trip, the ship's doctors said. The Veendam left New York 36 days ago for a South America tour.'
+ 'highlights': 'The elderly woman suffered from diabetes and hypertension, ship's doctors say .\nPreviously, 86 passengers had fallen ill on the ship, Agencia Brasil says .'}
+```
 
-The BCP-47 code for English as generally spoken in the United States is en-US and the BCP-47 code for English as generally spoken in the United Kingdom is en-GB. It is unknown if other varieties of English are represented in the data.
+The average token count for the articles and the highlights are provided below:
 
-## Who is/was involved in the dataset use and creation? 
-### Who are the dataset curators? 
+| Feature    | Mean Token Count |
+| ---------- | ---------------- |
+| Article    | 781              |
+| Highlights | 56               |
 
-The data was originally collected by Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom of Google DeepMind. Tomáš Kočiský and Phil Blunsom are also affiliated with the University of Oxford. They released scripts to collect and process the data into the question answering format. 
+### Data Fields
 
-Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, and Bing Xiang of IMB Watson and Çağlar Gu̇lçehre of Université de Montréal modified Hermann et al's collection scripts to restore the data to a summary format. They also produced both anonymized and non-anonymized versions.
+- `id`: a string containing the heximal formated SHA1 hash of the url where the story was retrieved from
+- `article`: a string containing the body of the news article 
+- `highlights`: a string containing the highlight of the article as written by the article author
 
-The code for the non-anonymized version is made publicly available by Abigail See of Stanford University, Peter J. Liu of Google Brain and Christopher D. Manning of Stanford University at <https://github.com/abisee/cnn-dailymail>. The work at Stanford University was supported by the DARPA DEFT ProgramAFRL contract no. FA8750-13-2-0040.
+### Data Splits
 
+The CNN/DailyMail dataset has 3 splits: _train_, _validation_, and _test_. Below are the statistics for Version 3.0.0 of the dataset.
 
-### Who are the language producers (who wrote the text / created the base content)?
+| Dataset Split | Number of Instances in Split                |
+| ------------- | ------------------------------------------- |
+| Train         | 287,113                                     |
+| Validation    | 13,368                                      |
+| Test          | 11,490                                      |
 
-The text was written by journalists at CNN and the Daily Mail. 
+## Dataset Creation
+
+### Curation Rationale
 
-### Who are the annotators?
+Version 1.0.0 aimed to support supervised neural methodologies for machine reading and question answering with a large amount of real natural language training data and released about 313k unique articles and nearly 1M Cloze style questions to go with the articles. Versions 2.0.0 and 3.0.0 changed the structure of the dataset to support summarization rather than question answering. Version 3.0.0 provided a non-anonymized version of the data, whereas both the previous versions were preprocessed to replace named entities with unique identifier labels. 
 
-No annotation was provided with the dataset.
+### Source Data
 
-## Data characteristics
+#### Initial Data Collection and Normalization
 
 The data consists of news articles and highlight sentences. In the question answering setting of the data, the articles are used as the context and entities are hidden one at a time in the highlight sentences, producing Cloze style questions where the goal of the model is to correctly guess which entity in the context has been hidden in the highlight. In the summarization setting, the highlight sentences are concatenated to form a summary of the article. The CNN articles were written between April 2007 and April 2015. The Daily Mail articles were written between June 2010 and April 2015. 
 
-### How was the data collected?
-
 The code for the original data collection is available at <https://github.com/deepmind/rc-data>. The articles were downloaded using archives of <www.cnn.com> and <www.dailymail.co.uk> on the Wayback Machine. Articles were not included in the Version 1.0.0 collection if they exceeded 2000 tokens. Due to accessibility issues with the Wayback Machine, Kyunghyun Cho has made the datasets available at <https://cs.nyu.edu/~kcho/DMQA/>. An updated version of the code that does not anonymize the data is available at <https://github.com/abisee/cnn-dailymail>. 
 
+Hermann et al provided their own tokenization script. The script provided by See uses the PTBTokenizer. It also lowercases the text and adds periods to lines missing them.
 
-### Normalization information
+#### Who are the source language producers?
 
-Hermann et al provided their own tokenization script. The script provided by See uses the PTBTokenizer. It also lowercases the text and adds periods to lines missing them.
+The text was written by journalists at CNN and the Daily Mail. 
 
-### Annotation process
+### Annotations
 
-No annotation was provided with the dataset.
+The dataset does not contain any additional annotations.
 
-## Dataset Structure
-### Splits, features, and labels
+#### Annotation process
 
-The CNN/DailyMail dataset has 3 splits: _train_, _validation_, and _test_. Below are the statistics for Version 3.0.0 of the dataset.
+[N/A]
 
-Dataset Split | Number of Instances in Split
---------------|--------------------------------------------
-Train | 287,113
-Validation | 13,368
-Test | 11,490
+#### Who are the annotators?
 
-Each data instance contains the following features: _article_, _highlights_, _id_.
+[N/A]
 
-Feature | Mean Token Count
---------|-----------------
-Article | 781 
-Highlights | 56
+### Personal and Sensitive Information
 
-### Span indices
+Version 3.0 is not anonymized, so individuals' names can be found in the dataset. Information about the original author is not included in the dataset.
 
-No span indices are included in this dataset.
+## Considerations for Using the Data
 
-### Example ID
+### Social Impact of Dataset
 
-An example ID is '0001d1afc246a7964130f43ae940af6bc6c57f01'. These are heximal formated SHA1 hashes of the urls where the stories were retrieved from.
+The purpose of this dataset is to help develop models that can summarize long paragraphs of text in one or two sentences.
 
-### Free text description for context (e.g. describe difference between title / selftext / body in Reddit data) and example
+This task is useful for efficiently presenting information given a large quantity of text. It should be made clear that any summarizations produced by models trained on this dataset are reflective of the language used in the articles, but are in fact automatically generated.
 
-For each ID, there is a string for the article, a string for the highlights, and a string for the id. See the [CNN / Daily Mail dataset viewer](https://huggingface.co/datasets/viewer/?dataset=cnn_dailymail&config=3.0.0) to explore more examples.
+### Discussion of Biases
 
-ID | Article | Hightlights 
----|---------|------------
-0054d6d30dbcad772e20b22771153a2a9cbeaf62 | (CNN) -- An American woman died aboard a cruise ship that docked at Rio de Janeiro on Tuesday, the same ship on which 86 passengers previously fell ill, according to the state-run Brazilian news agency, Agencia Brasil. The American tourist died aboard the MS Veendam, owned by cruise operator Holland America. Federal Police told Agencia Brasil that forensic doctors were investigating her death. The ship's doctors told police that the woman was elderly and suffered from diabetes and hypertension, according the agency. The other passengers came down with diarrhea prior to her death during an earlier part of the trip, the ship's doctors said. The Veendam left New York 36 days ago for a South America tour.	| The elderly woman suffered from diabetes and hypertension, ship's doctors say .\nPreviously, 86 passengers had fallen ill on the ship, Agencia Brasil says .
+[Bordia and Bowman (2019)](https://www.aclweb.org/anthology/N19-3002.pdf) explore measuring gender bias and debiasing techniques in the CNN / Dailymail dataset, the Penn Treebank, and WikiText-2. They find the CNN / Dailymail dataset to have a slightly lower gender bias based on their metric compared to the other datasets, but still show evidence of gender bias when looking at words such as 'fragile'.
 
-### Suggested metrics / models:
+Because the articles were written by and for people in the US and the UK, they will likely present specifically US and UK perspectives and feature events that are considered relevant to those populations during the time that the articles were published. 
 
-[Zhong et al (2020)](https://www.aclweb.org/anthology/2020.acl-main.552.pdf) report a ROUGE-1 score of 44.41. See the [Papers With Code leaderboard](https://paperswithcode.com/sota/document-summarization-on-cnn-daily-mail) for more models. 
+### Other Known Limitations
 
-## Known Limitations
-### Known social biases
+News articles have been shown to conform to writing conventions in which important information is primarily presented in the first third of the article [(Kryściński et al, 2019)](https://www.aclweb.org/anthology/D19-1051.pdf). [Chen et al (2016)](https://www.aclweb.org/anthology/P16-1223.pdf) conducted a manual study of 100 random instances of the first version of the dataset and found 25% of the samples to be difficult even for humans to answer correctly due to ambiguity and coreference errors. 
 
-[Bordia and Bowman (2019)](https://www.aclweb.org/anthology/N19-3002.pdf) explore measuring gender bias and debiasing techniques in the CNN / Dailymail dataset, the Penn Treebank, and WikiText-2. They find the CNN / Dailymail dataset to have a slightly lower gender bias based on their metric compared to the other datasets, but still show evidence of gender bias when looking at words such as 'fragile'.
+It should also be noted that machine-generated summarizations, even when extractive, may differ in truth values when compared to the original articles. 
 
-### Other known limitations
+## Additional Information
 
-News articles have been shown to conform to writing conventions in which important information is primarily presented in the first third of the article [(Kryściński et al, 2019)](https://www.aclweb.org/anthology/D19-1051.pdf). [Chen et al (2016)](https://www.aclweb.org/anthology/P16-1223.pdf) conducted a manual study of 100 random instances of the first version of the dataset and found 25% of the samples to be difficult even for humans to answer correctly due to ambiguity and coreference errors. 
+### Dataset Curators
+
+The data was originally collected by Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom of Google DeepMind. Tomáš Kočiský and Phil Blunsom are also affiliated with the University of Oxford. They released scripts to collect and process the data into the question answering format. 
+
+Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, and Bing Xiang of IMB Watson and Çağlar Gu̇lçehre of Université de Montréal modified Hermann et al's collection scripts to restore the data to a summary format. They also produced both anonymized and non-anonymized versions.
 
-## Licensing information
+The code for the non-anonymized version is made publicly available by Abigail See of Stanford University, Peter J. Liu of Google Brain and Christopher D. Manning of Stanford University at <https://github.com/abisee/cnn-dailymail>. The work at Stanford University was supported by the DARPA DEFT ProgramAFRL contract no. FA8750-13-2-0040.
+
+### Licensing Information
 
 The CNN / Daily Mail dataset version 1.0.0 is released under the [Apache-2.0 License](http://www.apache.org/licenses/LICENSE-2.0). 
 
+### Citation Information
+
+```
+@inproceedings{see-etal-2017-get,
+    title = "Get To The Point: Summarization with Pointer-Generator Networks",
+    author = "See, Abigail  and
+      Liu, Peter J.  and
+      Manning, Christopher D.",
+    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = jul,
+    year = "2017",
+    address = "Vancouver, Canada",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/P17-1099",
+    doi = "10.18653/v1/P17-1099",
+    pages = "1073--1083",
+    abstract = "Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves. In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways. First, we use a hybrid pointer-generator network that can copy words from the source text via pointing, which aids accurate reproduction of information, while retaining the ability to produce novel words through the generator. Second, we use coverage to keep track of what has been summarized, which discourages repetition. We apply our model to the CNN / Daily Mail summarization task, outperforming the current abstractive state-of-the-art by at least 2 ROUGE points.",
+}
+```
+
+```
+@inproceedings{DBLP:conf/nips/HermannKGEKSB15,
+  author={Karl Moritz Hermann and Tomás Kociský and Edward Grefenstette and Lasse Espeholt and Will Kay and Mustafa Suleyman and Phil Blunsom},
+  title={Teaching Machines to Read and Comprehend},
+  year={2015},
+  cdate={1420070400000},
+  pages={1693-1701},
+  url={http://papers.nips.cc/paper/5945-teaching-machines-to-read-and-comprehend},
+  booktitle={NIPS},
+  crossref={conf/nips/2015}
+}
+
+```
+
 ### Contributions
 
-Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@jplu](https://github.com/jplu), [@jbragg](https://github.com/jbragg), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.
+Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@jplu](https://github.com/jplu), [@jbragg](https://github.com/jbragg), [@patrickvonplaten](https://github.com/patrickvonplaten) and [@mcmillanmajora](https://github.com/mcmillanmajora) for adding this dataset.
+

cnn_dailymail.py

@@ -18,12 +18,14 @@
 from __future__ import absolute_import, division, print_function
 
 import hashlib
-import logging
 import os
 
 import datasets
 
 
+logger = datasets.logging.get_logger(__name__)
+
+
 _DESCRIPTION = """\
 CNN/DailyMail non-anonymized summarization dataset.
 
@@ -110,7 +112,7 @@ def _get_url_hashes(path):
         try:
             u = u.encode("utf-8")
         except UnicodeDecodeError:
-            logging.error("Cannot hash url: %s", u)
+            logger.error("Cannot hash url: %s", u)
         h.update(u)
         return h.hexdigest()
 
@@ -130,7 +132,7 @@ def _find_files(dl_paths, publisher, url_dict):
     elif publisher == "dm":
         top_dir = os.path.join(dl_paths["dm_stories"], "dailymail", "stories")
     else:
-        logging.fatal("Unsupported publisher: %s", publisher)
+        logger.fatal("Unsupported publisher: %s", publisher)
     files = sorted(os.listdir(top_dir))
 
     ret_files = []
@@ -151,7 +153,7 @@ def _subset_filenames(dl_paths, split):
     elif split == datasets.Split.TEST:
         urls = _get_url_hashes(dl_paths["test_urls"])
     else:
-        logging.fatal("Unsupported split: %s", split)
+        logger.fatal("Unsupported split: %s", split)
     cnn = _find_files(dl_paths, "cnn", urls)
     dm = _find_files(dl_paths, "dm", urls)
     return cnn + dm