Reddit dataset card additions (#3781)

* Proposed changes are based on the official paper of the dataset. The name given to the dataset by the authors is Webis-TLDR-17 (corpus), so perhaps it should be modified as well.

* README.md.bak removed

* mention Webis-TLDR-17 in the title

Co-authored-by: anna-kay <annakougioumtz@gmail.com>
Co-authored-by: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com>

Commit from https://github.com/huggingface/datasets/commit/278db9f51222842cb591b64dbdc3f1f264667aa7

README.md

@@ -2,10 +2,10 @@
 languages:
 - en
 paperswithcode_id: reddit
-pretty_name: Reddit
+pretty_name: Reddit Webis-TLDR-17
 ---
 
-# Dataset Card for "reddit"
+# Dataset Card for Reddit Webis-TLDR-17
 
 ## Table of Contents
 - [Dataset Description](#dataset-description)
@@ -33,9 +33,9 @@ pretty_name: Reddit
 
 ## Dataset Description
 
-- **Homepage:** [https://github.com/webis-de/webis-tldr-17-corpus](https://github.com/webis-de/webis-tldr-17-corpus)
-- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Homepage:** [https://webis.de/data/webis-tldr-17.html](https://webis.de/data/webis-tldr-17.html) 
+- **Repository:** [https://github.com/webis-de/webis-tldr-17-corpus](https://github.com/webis-de/webis-tldr-17-corpus)
+- **Paper:** [https://aclanthology.org/W17-4508]
 - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 - **Size of downloaded dataset files:** 2996.31 MB
 - **Size of the generated dataset:** 18063.11 MB
@@ -43,7 +43,7 @@ pretty_name: Reddit
 
 ### Dataset Summary
 
-This corpus contains preprocessed posts from the Reddit dataset.
+This corpus contains preprocessed posts from the Reddit dataset (Webis-TLDR-17).
 The dataset consists of 3,848,330 posts with an average length of 270 words for content,
 and 28 words for the summary.
 
@@ -52,11 +52,20 @@ Content is used as document and summary is used as summary.
 
 ### Supported Tasks and Leaderboards
 
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+Summarization (abstractive)
+
+Known ROUGE scores achieved for the Webis-TLDR-17:
+
+| Model | ROUGE-1 | ROUGE-2 | ROUGE-L | Paper/Source |
+|-------|-------|-------|-------|------:|
+| Transformer + Copy (Gehrmann et al., 2019) | 22 | 6 | 17 | Generating Summaries with Finetuned Language Models | 	
+| Unified VAE + PGN (Choi et al., 2019) |	19 | 4 | 15 | VAE-PGN based Abstractive Model in Multi-stage Architecture for Text Summarization | 	
+
+(Source: https://github.com/sebastianruder/NLP-progress/blob/master/english/summarization.md)
 
 ### Languages
 
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+English 
 
 ## Dataset Structure
 
@@ -104,21 +113,26 @@ The data fields are the same among all splits.
 |-------|------:|
 |default|3848330|
 
+This corpus does not contain a separate test set. Thus it is up to the users to divide the corpus into appropriate training, validation and test sets.
+
 ## Dataset Creation
 
 ### Curation Rationale
 
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+In the scope of the task of absractive summarization the creators of the Webis-TLDR-17 propose mining social media for author-provided summaries and taking advantage of the common practice of appending a "TL;DR" to long posts. A large Reddit crawl was used to yield the Webis-TLDR-17 corpus. This dataset intends to complement the existing summarization corpora primarily from the news genre.
 
 ### Source Data
 
+Reddit subreddits posts (submissions & comments) containing "TL;DR" from 2006 to 2016. Multiple subreddits are included.
+
 #### Initial Data Collection and Normalization
 
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+Initial data: a set of 286 million submissions and 1.6 billion comments posted to Reddit between 2006 and 2016.
+Then a five-step pipeline of consecutive filtering steps was applied.
 
 #### Who are the source language producers?
 
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+The contents of the dataset are produced by human authors, bot-generated content was eliminated by filtering out all bot accounts with the help of an extensive list provided by the Reddit community, as well as manual inspection of cases where the user name contained the substring "bot."
 
 ### Annotations
 
@@ -138,7 +152,7 @@ The data fields are the same among all splits.
 
 ### Social Impact of Dataset
 
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+This dataset has been created to serve as a source of large-scale summarization training data. It is primarily geared towards the automatic abstractive summarization task, that can be considered one of the most challenging variants of automatic summarization. It also aims to tackle the lack of genre diversity in the summarization datasets (most are news-related).
 
 ### Discussion of Biases
 
@@ -146,13 +160,15 @@ The data fields are the same among all splits.
 
 ### Other Known Limitations
 
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+Reddit users write TL;DRs with various intentions, such as providing a “true” summary, asking questions or for help, or forming judgments and conclusions. As noted in the paper introducing the dataset, although the first kind of TL;DR posts are most important for training summarization models, yet, the latter allow for various alternative summarization-related tasks.
+
+Although filtering was performed abusive language maybe still be present. 
 
 ## Additional Information
 
 ### Dataset Curators
 
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+Michael Völske, Martin Potthast, Shahbaz Syed, Benno Stein
 
 ### Licensing Information