Update README.md

README.md

@@ -251,7 +251,7 @@ Yoruba | yo | https://www.bbc.com/yoruba | 6350 | 793 | 793 | 7936 |
 
 ### Curation Rationale
 
-[More information needed](https://github.com/csebuetnlp/xl-sum)
+State-of-the-art text summarization models are heavily data-driven, i.e., a large number of article-summary pairs are required to train them effectively. As a result, abstractive summarization has centered around the English language, as most large abstractive summarization datasets are available in English only. Though there have been some recent efforts for curating multilingual abstractive summarization datasets, they are limited in terms of the number of languages covered, the number of training samples, or both. To this end, we curate **XL-Sum**, a large-scale abstractive summarization dataset of 1.35 million news articles from 45 languages crawled from the British Broadcasting Corporation website.
 
 ### Source Data
 
@@ -259,50 +259,55 @@ Yoruba | yo | https://www.bbc.com/yoruba | 6350 | 793 | 793 | 7936 |
 
 #### Initial Data Collection and Normalization
 
-[Detailed in the paper](https://aclanthology.org/2021.findings-acl.413/) 
+We designed a crawler to recursively crawl pages starting from the homepage by visiting different article links present in each page visited. We were able to take advantage of the fact that all BBC sites have somewhat similar structures, and were able to scrape articles from all sites. We discarded pages with no textual contents (mostly pages consisting of multimedia contents) before further processing. We designed a number of heuristics to make the extraction effective by carefully examining the HTML structures of the crawled pages:
 
+1. The desired summary must be present within the beginning two paragraphs of an article.
+2. The summary paragraph must have some portion of texts in bold format.
+3. The summary paragraph may contain some hyperlinks that may not be bold. The proportion of bold texts and hyperlinked texts to the total length of the paragraph in consideration must be at least 95\%.
+4. All texts except the summary and the headline must be included in the input text (including image captions).
+5. The input text must be at least twice as large as the summary.
 
-#### Who are the source language producers?
 
-[Detailed in the paper](https://aclanthology.org/2021.findings-acl.413/) 
 
+#### Who are the source language producers?
 
-### Annotations
+[BBC News Editorial Team](https://www.bbc.co.uk/ws/languages) 
 
-[Detailed in the paper](https://aclanthology.org/2021.findings-acl.413/) 
 
+### Annotations
 
 #### Annotation process
 
-[Detailed in the paper](https://aclanthology.org/2021.findings-acl.413/) 
+BBC typically provides a summary of a whole article in the form of a bold paragraph containing one or two sentences at the beginning of each article. These summaries are written professionally by the authors of the articles in order to convey its main story within one small paragraph. This is in contrast to the headline which serves to draw the attention of viewers into reading the article. We used the bold texts as summary and the rest of the article as input.
 
 #### Who are the annotators?
 
-[Detailed in the paper](https://aclanthology.org/2021.findings-acl.413/) 
+[BBC News Editorial Team](https://www.bbc.co.uk/ws/languages) 
 
 ### Personal and Sensitive Information
 
-[More information needed](https://github.com/csebuetnlp/xl-sum)
+Meta-information like author names are discarded. However, we cannot guarantee removal of all personal information. 
 
 ## Considerations for Using the Data
 
 ### Social Impact of Dataset
 
-[More information needed](https://github.com/csebuetnlp/xl-sum)
+We believe that our efforts in this work will encourage the community to push the boundaries of abstractive text summarization beyond the English language, especially for low and mid-resource languages, bringing technological advances to communities of these languages that have been traditionally under-served.
+
 
 ### Discussion of Biases
 
-[More information needed](https://github.com/csebuetnlp/xl-sum)
+Human evaluation showed most languages had a high percentage of good summaries in the upper nineties, almost none of the summaries contained any conflicting information, while about one-third on average had information that was not directly inferrable from the source article.
 
 ### Other Known Limitations
 
-[More information needed](https://github.com/csebuetnlp/xl-sum)
+The dataset is limited to news domain only.
 
 ## Additional Information
 
 ### Dataset Curators
 
-[More information needed](https://github.com/csebuetnlp/xl-sum)
+[Authors of this paper](https://aclanthology.org/2021.findings-acl.413)
 
 ### Licensing Information