NewsQs: Multi-Source Question Generation for the Inquiring Mind
<|reference_start|>NewsQs: Multi-Source Question Generation for the Inquiring Mind: 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.<|reference_end|>
@article{hwang2024newsqs:,
    title={NewsQs: Multi-Source Question Generation for the Inquiring Mind},
    author={Alyssa Hwang, Kalpit Dixit, Miguel Ballesteros, Yassine Benajiba,
  Vittorio Castelli, Markus Dreyer, Mohit Bansal, Kathleen McKeown},
    journal={arXiv preprint arXiv:2402.18479},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.18479},
    primaryClass={cs.CL}
}