NewsQs: Multi-Source Question Generation for the Inquiring Mind

Alyssa Hwang, Kalpit Dixit, Miguel Ballesteros, Yassine Benajiba, Vittorio Castelli +3 more
2/28/2024
cs.CL

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Abstract

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.

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Cite this paper

@article{hwang2024newsqs,
  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},
  year   = {2024},
  eprint = {2402.18479},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2402.18479v2}
}

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