Update README.md

README.md

@@ -3,4 +3,38 @@ task_categories:
 - summarization
 - text-generation
 - text2text-generation
----
+---
+
+# DivSumm summarization dataset
+
+Dataset introduced in the paper: Analyzing the Dialect Diversity in Multi-document Summaries (COLING 2022)
+_Olubusayo Olabisi, Aaron Hudson, Antonie Jetter, Ameeta Agrawal_
+
+
+DivSumm is a novel dataset consisting of dialect-diverse tweets and human-written extractive and abstractive summaries. It consists of 90 tweets each on 25 topics in multiple English dialects (African-American, Hispanic and White), and two reference summaries per input.
+
+
+
+## Directories
+
+input_docs - 90 tweets per topic evenly distributed among 3 dialects; total 25 topics
+
+abstractive - Two annotators were asked to summarize each topic in 5 sentences using their own words. 
+
+extractive - Two annotators were asked to select 5 tweets from each topic that summarized the input tweets.
+
+
+## Paper
+
+
+You can find our paper [here](https://aclanthology.org/2022.coling-1.542/). If you use this dataset in your work, please cite our paper:
+
+
+    @inproceedings{olabisi-etal-2022-analyzing,
+        title = "Analyzing the Dialect Diversity in Multi-document Summaries",
+        author = "Olabisi, Olubusayo  and Hudson, Aaron  and Jetter, Antonie  and Agrawal, Ameeta",
+        booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
+        month = oct,
+        year = "2022",
+    }
+