# Exploring Content Selection in Summarization of Novel Chapters

arXiv: https://arxiv.org/abs/2005.01840 · HTML: https://mlsanity.com/paper/2005.01840/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2020.

## Built on by (most cited first, 29 of 29)

- [Recursively Summarizing Books with Human Feedback](https://mlsanity.com/paper/2109.10862/) (2021)
- [BookSum: A Collection of Datasets for Long-form Narrative Summarization](https://mlsanity.com/paper/2105.08209/) (2021)
- [A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methods](https://mlsanity.com/paper/2403.02901/) (2024)
- [Fantastic Questions and Where to Find Them: FairytaleQA -- An Authentic Dataset for Narrative Comprehension](https://mlsanity.com/paper/2203.13947/) (2022)
- [SummScreen: A Dataset for Abstractive Screenplay Summarization](https://mlsanity.com/paper/2104.07091/) (2021)
- [SQuALITY: Building a Long-Document Summarization Dataset the Hard Way](https://mlsanity.com/paper/2205.11465/) (2022)
- ["Let Your Characters Tell Their Story": A Dataset for Character-Centric Narrative Understanding](https://mlsanity.com/paper/2109.05438/) (2021)
- [Learning to Revise References for Faithful Summarization](https://mlsanity.com/paper/2204.10290/) (2022)
- [Personality Understanding of Fictional Characters during Book Reading](https://mlsanity.com/paper/2305.10156/) (2023)
- [SNaC: Coherence Error Detection for Narrative Summarization](https://mlsanity.com/paper/2205.09641/) (2022)
- [Reading Subtext: Evaluating Large Language Models on Short Story Summarization with Writers](https://mlsanity.com/paper/2403.01061/) (2024)
- [TVShowGuess: Character Comprehension in Stories as Speaker Guessing](https://mlsanity.com/paper/2204.07721/) (2022)
- [StreamHover: Livestream Transcript Summarization and Annotation](https://mlsanity.com/paper/2109.05160/) (2021)
- [A Survey of Machine Narrative Reading Comprehension Assessments](https://mlsanity.com/paper/2205.00299/) (2022)
- [Select and Summarize: Scene Saliency for Movie Script Summarization](https://mlsanity.com/paper/2404.03561/) (2024)
- [NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization](https://mlsanity.com/paper/2505.24575/) (2025)
- [Generating EDU Extracts for Plan-Guided Summary Re-Ranking](https://mlsanity.com/paper/2305.17779/) (2023)
- [The Current State of Summarization](https://mlsanity.com/paper/2305.04853/) (2023)
- [CREATIVESUMM: Shared Task on Automatic Summarization for Creative Writing](https://mlsanity.com/paper/2211.05886/) (2022)
- [NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarization](https://mlsanity.com/paper/2212.01476/) (2022)
- [Hybrid Long Document Summarization using C2F-FAR and ChatGPT: A Practical Study](https://mlsanity.com/paper/2306.01169/) (2023)
- [Fine-Tuned LLMs are "Time Capsules" for Tracking Societal Bias Through Books](https://mlsanity.com/paper/2502.05331/) (2025)
- [Previously on the Stories: Recap Snippet Identification for Story Reading](https://mlsanity.com/paper/2402.07271/) (2024)
- [The State and Fate of Summarization Datasets: A Survey](https://mlsanity.com/paper/2411.04585/) (2024)
- [Contrastive Learning with Narrative Twins for Modeling Story Salience](https://mlsanity.com/paper/2601.07765/) (2026)
- [AbLit: A Resource for Analyzing and Generating Abridged Versions of English Literature](https://mlsanity.com/paper/2302.06579/) (2023)
- [Tell, Don't Show: Leveraging Language Models' Abstractive Retellings to Model Literary Themes](https://mlsanity.com/paper/2505.23166/) (2025)
- [Novel Chapter Abstractive Summarization using Spinal Tree Aware Sub-Sentential Content Selection](https://mlsanity.com/paper/2211.04903/) (2022)
- [Is the Top Still Spinning? Evaluating Subjectivity in Narrative Understanding](https://mlsanity.com/paper/2504.01132/) (2025)

## Abstract (arXiv)

We present a new summarization task, generating summaries of novel chapters using summary/chapter pairs from online study guides. This is a harder task than the news summarization task, given the chapter length as well as the extreme paraphrasing and generalization found in the summaries. We focus on extractive summarization, which requires the creation of a gold-standard set of extractive summaries. We present a new metric for aligning reference summary sentences with chapter sentences to create gold extracts and also experiment with different alignment methods. Our experiments demonstrate significant improvement over prior alignment approaches for our task as shown through automatic metrics and a crowd-sourced pyramid analysis. We make our data collection scripts available at https://github.com/manestay/novel-chapter-dataset .