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arXiv · 2402.18479

NewsQs: Multi-Source Question Generation for the Inquiring Mind

Alyssa Hwang·Kalpit Dixit·Miguel Ballesteros·Yassine Benajiba·Vittorio Castelli·Markus Dreyer·Mohit Bansal·Kathleen McKeown
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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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Alyssa Hwang, Kalpit Dixit, Miguel Ballesteros, Yassine Benajiba, Vittorio Castelli, Markus Dreyer, Mohit Bansal, Kathleen McKeown. 2024-02-28. NewsQs: Multi-Source Question Generation for the Inquiring Mind. https://arxiv.org/abs/2402.18479

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