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NewsQs: Multi-Source Question Generation for the Inquiring Mind

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arxiv 2402.18479 v2 pith:IVKMN6YH submitted 2024-02-28 cs.CL

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 This is my paper
classification cs.CL
keywords modeldatasetnewsnewsqsquestionshumanmulti-documentsummarization
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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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