Abstract
Understanding narrative flow and text coherence in long-form documents (novels) remains an open problem in NLP.To gain insight, we explore the task of chapter ordering, reconstructing the original order of chapters in novel given a random permutation of the text. This can be seen as extending the well-known sentence ordering task to vastly larger documents: our task deals with over 9,000 novels with an average of twenty chapters each, versus standard sentence ordering datasets averaging only 5-8 sentences. We formulate the task of reconstructing order as a constraint solving problem, using minimum feedback arc set and traveling salesman problem optimization criteria, where the weights of the graph are generated based on models for character occurrences and chapter boundary detection, using relational chapter scores derived from RoBERTa. Our best methods yield a Spearman correlation of 0.59 on this novel and challenging task, substantially above baseline.- Anthology ID:
- 2022.emnlp-main.253
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3838–3848
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.253
- DOI:
- Cite (ACL):
- Allen Kim and Steve Skiena. 2022. Chapter Ordering in Novels. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 3838–3848, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Chapter Ordering in Novels (Kim & Skiena, EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.emnlp-main.253.pdf