We present NewsQs (news-cues), a dataset that provides question-answer pairs for multiple news documents. To create NewsQs, we augment a traditional multi-document summarization dataset with questions automatically generated by a T5-Large model fine-tuned on FAQ-style news articles from the News On the Web corpus. We show that fine-tuning a model with control codes produces questions that are judged acceptable more often than the same model without them as measured through human evaluation. We use a QNLI model with high correlation with human annotations to filter our data. We release our final dataset of high-quality questions, answers, and document clusters as a resource for future work in query-based multi-document summarization.
@article{arxiv.2402.18479,
title = {NewsQs: Multi-Source Question Generation for the Inquiring Mind},
author = {Alyssa Hwang and Kalpit Dixit and Miguel Ballesteros and Yassine Benajiba and Vittorio Castelli and Markus Dreyer and Mohit Bansal and Kathleen McKeown},
journal= {arXiv preprint arXiv:2402.18479},
year = {2024}
}