论文标题
信息查询的摘要问题生成
Summary-Oriented Question Generation for Informational Queries
论文作者
论文摘要
用户经常向问题回答（QA）系统提出简单的Factoid问题，从而减弱了支持更复杂问题的无数作品的影响。提示使用自动生成建议的问题（SQS）的用户可以提高用户对质量检查系统功能的理解，从而促进更有效的使用。我们旨在提出专注于主要文档主题的自我解释性问题，并在适当的长度段落中可以回答。我们通过使用基于BERT的Pointer-Menerator网络（NQ）数据集训练有素来满足这些要求。我们的模型显示了NQ数据集（20.1 BLEU-4）上SQ生成的SOTA性能。由于缺乏黄金问题，我们进一步将模型应用于室外新闻文章，并通过质量检查系统评估，并证明我们的模型为新闻文章提供了更好的SQ-通过人类评估进一步确认。
Users frequently ask simple factoid questions for question answering (QA) systems, attenuating the impact of myriad recent works that support more complex questions. Prompting users with automatically generated suggested questions (SQs) can improve user understanding of QA system capabilities and thus facilitate more effective use. We aim to produce self-explanatory questions that focus on main document topics and are answerable with variable length passages as appropriate. We satisfy these requirements by using a BERT-based Pointer-Generator Network trained on the Natural Questions (NQ) dataset. Our model shows SOTA performance of SQ generation on the NQ dataset (20.1 BLEU-4). We further apply our model on out-of-domain news articles, evaluating with a QA system due to the lack of gold questions and demonstrate that our model produces better SQs for news articles -- with further confirmation via a human evaluation.
