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NewsQs: Multi-Source Question Generation for the Inquiring Mind (2402.18479v2)
Published 28 Feb 2024 in cs.CL
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.
- YAKE! Keyword extraction from single documents using multiple local features. Information Sciences, 509:257–289. Publisher: Elsevier.
- Yake! collection-independent automatic keyword extractor. In European Conference on Information Retrieval, pages 806–810. Springer.
- YAKE! Collection-Independent Automatic Keyword Extractor. In Gabriella Pasi, Benjamin Piwowarski, Leif Azzopardi, and Allan Hanbury, editors, Advances in Information Retrieval, volume 10772, pages 806–810. Springer International Publishing, Cham. Series Title: Lecture Notes in Computer Science.
- CONSISTENT: Open-Ended Question Generation From News Articles.
- Mark Davies. 2022. Corpus of News on the Web (NOW).
- The Hitchhiker’s Guide to Testing Statistical Significance in Natural Language Processing. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1383–1392. Association for Computational Linguistics. Event-place: Melbourne, Australia.
- Question Generation for Question Answering. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 866–874, Copenhagen, Denmark. Association for Computational Linguistics.
- DUC. 2007. Document Understanding Conferences - Past Data.
- A feasibility study of answer-agnostic question generation for education. In Findings of the Association for Computational Linguistics: ACL 2022, pages 1919–1926, Dublin, Ireland. Association for Computational Linguistics.
- Multi-News: A Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1074–1084, Florence, Italy. Association for Computational Linguistics.
- ELI5: Long Form Question Answering. arXiv:1907.09190 [cs]. ArXiv: 1907.09190.
- A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portal. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1302–1308, Online. Association for Computational Linguistics.
- DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications. ArXiv:1711.05073 [cs].
- Hurdles to progress in long-form question answering. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4940–4957, Online. Association for Computational Linguistics.
- AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization.
- BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension.
- Chin-Yew Lin. 2004. ROUGE: A package for automatic evaluation of summaries. In Text Summarization Branches Out, pages 74–81, Barcelona, Spain. Association for Computational Linguistics.
- KILT: a benchmark for knowledge intensive language tasks. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2523–2544, Online. Association for Computational Linguistics.
- Language Models are Unsupervised Multitask Learners. page 24.
- Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. arXiv:1910.10683 [cs, stat]. ArXiv: 1910.10683.
- Multitask Prompted Training Enables Zero-Shot Task Generalization. arXiv:2110.08207 [cs]. ArXiv: 2110.08207.
- ASQA: Factoid Questions Meet Long-Form Answers. arXiv:2204.06092 [cs]. ArXiv: 2204.06092.
- SQuALITY: Building a Long-Document Summarization Dataset the Hard Way. arXiv:2205.11465 [cs]. ArXiv: 2205.11465.
- BERTScore: Evaluating Text Generation with BERT.
- Alyssa Hwang (10 papers)
- Kalpit Dixit (3 papers)
- Miguel Ballesteros (70 papers)
- Yassine Benajiba (21 papers)
- Vittorio Castelli (24 papers)
- Markus Dreyer (14 papers)
- Mohit Bansal (304 papers)
- Kathleen McKeown (85 papers)