Recent advancements in open-domain question answering (ODQA), i.e., finding answers from large open-domain corpus like Wikipedia, have led to human-level performance on many datasets. However, progress in QA over book stories (Book QA) lags behind despite its similar task formulation to ODQA. This work provides a comprehensive and quantitative analysis about the difficulty of Book QA: (1) We benchmark the research on the NarrativeQA dataset with extensive experiments with cutting-edge ODQA techniques. This quantifies the challenges Book QA poses, as well as advances the published state-of-the-art with a $\sim$7\% absolute improvement on Rouge-L. (2) We further analyze the detailed challenges in Book QA through human studies.\footnote{\url{https://github.com/gorov/BookQA}.} Our findings indicate that the event-centric questions dominate this task, which exemplifies the inability of existing QA models to handle event-oriented scenarios.

本篇研究对开放领域问答(ODQA)与书籍问答(Book QA)之间的难度差异进行了全面且量化的分析，结合了先进的ODQA技术并在NarrativeQA数据集上进行了实验，对于现有QA模型在处理事件中心问题的局限性有进一步的人工研究，发现现有QA无法很好地处理以事件为本的场景，而本篇研究提出的一种新技术使得书籍问答的Rouge-L有约7% 的绝对改进。

采用尖端开放领域问答技术的叙述式问题回答：综合研究