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Understanding stories is a challenging reading comprehension problem formachines as it requires reading a large volume of text and following long-rangedependencies. In this paper, we introduce the Shmoop Corpus: a dataset of 231stories that are paired with detailed multi-paragraph summaries for eachindividual chapter (7,234 chapters), where the summary is chronologicallyaligned with respect to the story chapter. From the corpus, we construct a setof common NLP tasks, including Cloze-form question answering and a simplifiedform of abstractive summarization, as benchmarks for reading comprehension onstories. We then show that the chronological alignment provides a strongsupervisory signal that learning-based methods can exploit leading tosignificant improvements on these tasks. We believe that the unique structureof this corpus provides an important foothold towards making machine storycomprehension more approachable.

The Shmoop Corpus: A Dataset of Stories with Loosely Aligned Summaries