Reading comprehension continues to be a crucial research focus in the NLP
community. Recent advances in Machine Reading Comprehension (MRC) have mostly
centered on literal comprehension, referring to the surface-level understanding
of content. In this work, we focus on the next level - interpretive
comprehension, with a particular emphasis on inferring the themes of a
narrative text. We introduce the first dataset specifically designed for
interpretive comprehension of educational narratives, providing corresponding
well-edited theme texts. The dataset spans a variety of genres and cultural
origins and includes human-annotated theme keywords with varying levels of
granularity. We further formulate NLP tasks under different abstractions of
interpretive comprehension toward the main idea of a story. After conducting
extensive experiments with state-of-the-art methods, we found the task to be
both challenging and significant for NLP research. The dataset and source code
have been made publicly available to the research community at
this https URL

本研究关注阅读理解的新一级别 —— 解释性理解，特别强调推断叙述文本的主题。我们介绍了第一个专门设计用于解释性理解教育叙述文本的数据集，并提供相应的主题文本。该数据集涵盖了各种流派和文化背景，并包含了人工注释的主题关键词，具有不同层次的细粒度。通过采用不同的解释性理解抽象形式，我们进一步对自然语言处理任务进行了形式化，以研究故事的主要思想。经过与先进方法的大量实验，我们发现该任务对自然语言处理研究来说具有挑战性和重要性。该数据集和源代码已公开提供给研究社区。