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

arXiv: https://arxiv.org/abs/1912.13082 · HTML: https://mlsanity.com/paper/1912.13082/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2019.

## Built on by (most cited first, 8 of 8)

- [English Machine Reading Comprehension Datasets: A Survey](https://mlsanity.com/paper/2101.10421/) (2021)
- [Memory and Knowledge Augmented Language Models for Inferring Salience in Long-Form Stories](https://mlsanity.com/paper/2109.03754/) (2021)
- [Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding](https://mlsanity.com/paper/2310.18783/) (2023)
- [A Temporal Variational Model for Story Generation](https://mlsanity.com/paper/2109.06807/) (2021)
- [Narrative Theory-Driven LLM Methods for Automatic Story Generation and Understanding: A Survey](https://mlsanity.com/paper/2602.15851/) (2026)
- [GNAT: A General Narrative Alignment Tool](https://mlsanity.com/paper/2311.03627/) (2023)
- [AbLit: A Resource for Analyzing and Generating Abridged Versions of English Literature](https://mlsanity.com/paper/2302.06579/) (2023)
- [Recent Advances in Multi-Choice Machine Reading Comprehension: A Survey on Methods and Datasets](https://mlsanity.com/paper/2408.02114/) (2024)

## Abstract (arXiv)

Understanding stories is a challenging reading comprehension problem for machines as it requires reading a large volume of text and following long-range dependencies. In this paper, we introduce the Shmoop Corpus: a dataset of 231 stories that are paired with detailed multi-paragraph summaries for each individual chapter (7,234 chapters), where the summary is chronologically aligned with respect to the story chapter. From the corpus, we construct a set of common NLP tasks, including Cloze-form question answering and a simplified form of abstractive summarization, as benchmarks for reading comprehension on stories. We then show that the chronological alignment provides a strong supervisory signal that learning-based methods can exploit leading to significant improvements on these tasks. We believe that the unique structure of this corpus provides an important foothold towards making machine story comprehension more approachable.