Chapter Ordering in Novels

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.