|
--- |
|
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", |
|
} |
|
|
|
|
