NewsQs: Multi-Source Question Generation for the Inquiring Mind
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 22 references that are in the map. First posted on arXiv 28 Feb 2024.
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10 of 22 references.
References · 10
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
- 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
- 2020KILT: a Benchmark for Knowledge Intensive Language Taskscited by 571
- 2019ELI5: Long Form Question Answeringcited by 567
- 2019Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Modelcited by 431
- 2021Hurdles to Progress in Long-form Question Answeringcited by 166
- 2017DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applicationscited by 110
- 2020A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portalcited by 72
- 2020AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarizationcited by 42
- 2022CONSISTENT: Open-Ended Question Generation From News Articlescited by 6
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