Bisi/DivSumm

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. 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", }