# Previously on the Stories: Recap Snippet Identification for Story Reading

arXiv: https://arxiv.org/abs/2402.07271 · HTML: https://mlsanity.com/paper/2402.07271/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 32 references that are in the map.
- First posted on arXiv 11 Feb 2024.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [The NarrativeQA Reading Comprehension Challenge](https://mlsanity.com/paper/1712.07040/) (2017)
- [Text and Code Embeddings by Contrastive Pre-Training](https://mlsanity.com/paper/2201.10005/) (2022)
- [ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer](https://mlsanity.com/paper/2105.11741/) (2021)
- [Whitening Sentence Representations for Better Semantics and Faster Retrieval](https://mlsanity.com/paper/2103.15316/) (2021)
- [Text Classification Algorithms: A Survey](https://mlsanity.com/paper/1904.08067/) (2019)
- [SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrieval](https://mlsanity.com/paper/2207.02578/) (2022)
- [Fantastic Questions and Where to Find Them: FairytaleQA -- An Authentic Dataset for Narrative Comprehension](https://mlsanity.com/paper/2203.13947/) (2022)
- ["Let Your Characters Tell Their Story": A Dataset for Character-Centric Narrative Understanding](https://mlsanity.com/paper/2109.05438/) (2021)
- [Word Embeddings: A Survey](https://mlsanity.com/paper/1901.09069/) (2019)
- [Movie Plot Analysis via Turning Point Identification](https://mlsanity.com/paper/1908.10328/) (2019)
- [Exploring Content Selection in Summarization of Novel Chapters](https://mlsanity.com/paper/2005.01840/) (2020)
- [Screenplay Summarization Using Latent Narrative Structure](https://mlsanity.com/paper/2004.12727/) (2020)
- [Personality Understanding of Fictional Characters during Book Reading](https://mlsanity.com/paper/2305.10156/) (2023)
- [Few-Shot Character Understanding in Movies as an Assessment to Meta-Learning of Theory-of-Mind](https://mlsanity.com/paper/2211.04684/) (2022)
- [TVShowGuess: Character Comprehension in Stories as Speaker Guessing](https://mlsanity.com/paper/2204.07721/) (2022)
- [A Survey of Machine Narrative Reading Comprehension Assessments](https://mlsanity.com/paper/2205.00299/) (2022)
- [Plot Retrieval as an Assessment of Abstract Semantic Association](https://mlsanity.com/paper/2311.01666/) (2023)
- [GNAT: A General Narrative Alignment Tool](https://mlsanity.com/paper/2311.03627/) (2023)
- [Analyzing Film Adaptation through Narrative Alignment](https://mlsanity.com/paper/2311.04020/) (2023)

## Built on by (most cited first, 3 of 3)

- [Fine-Grained Modeling of Narrative Context: A Coherence Perspective via Retrospective Questions](https://mlsanity.com/paper/2402.13551/) (2024)
- [The Stochastic Parrot on LLM's Shoulder: A Summative Assessment of Physical Concept Understanding](https://mlsanity.com/paper/2502.08946/) (2025)
- [SitEmb-v1.5: Improved Context-Aware Dense Retrieval for Semantic Association and Long Story Comprehension](https://mlsanity.com/paper/2508.01959/) (2025)

## Abstract (arXiv)

Similar to the "previously-on" scenes in TV shows, recaps can help book reading by recalling the readers' memory about the important elements in previous texts to better understand the ongoing plot. Despite its usefulness, this application has not been well studied in the NLP community. We propose the first benchmark on this useful task called Recap Snippet Identification with a hand-crafted evaluation dataset. Our experiments show that the proposed task is challenging to PLMs, LLMs, and proposed methods as the task requires a deep understanding of the plot correlation between snippets.