Modelling Suspense in Short Stories as Uncertainty Reduction over Neural Representation (2020)

Abstract: Suspense is a crucial ingredient of narrative fiction, engaging readers and making stories compelling. While there is a vast theoretical literature on suspense, it is computationally not well understood. We compare two ways for modelling suspense: surprise, a backward-looking measure of how unexpected the current state is given the story so far; and uncertainty reduction, a forward-looking measure of how unexpected the continuation of the story is. Both can be computed either directly over story representations or over their probability distributions. We propose a hierarchical language model that encodes stories and computes surprise and uncertainty reduction. Evaluating against short stories annotated with human suspense judgements, we find that uncertainty reduction over representations is the best predictor, resulting in near human accuracy. We also show that uncertainty reduction can be used to predict suspenseful events in movie synopses.

## introduction as current nlp research expands to include longer, fictional texts, it becomes increasingly important to understand narrative structure. previous work has analyzed narratives at the level of characters and plot events (e.g., @xcite . however, systems that process or generate narrative texts also have to take into account what makes stories compelling and enjoyable. we follow a literary tradition that makes and then? @xcite the primary question and regards suspense as a crucial factor of storytelling. studies show that suspense is important for keeping readers' attention @xcite , promotes readers' immersion and suspension of disbelief @xcite , and plays a big part in making stories enjoyable and interesting @xcite . computationally less well understood, suspense has only sporadically been used in story generation systems @xcite . suspense, intuitively, is a feeling of anticipation that something risky or dangerous will occur; this includes the idea both of uncertainty and jeopardy. take the play romeo and juliet: dramatic suspense is created throughout -the initial duel, the meeting at the masquerade ball, the marriage, the fight in which tybalt is killed, and the sleeping potions leading to the death of romeo and juliet. at each moment, the audience is invested in something being at stake and wonders how it will end. this paper aims to model suspense in computational terms, with the ultimate goal of making it deployable in nlp systems that analyze or generate narrative fiction. we start from the assumption that concepts developed in psycholinguistics to model human language processing at the word level @xcite can be generalised to the story level to capture suspense, the hale model. this assumption is similar concepts to model suspense in games @xcite , the ely model. common to both approaches is the idea that suspense is a form of expectation: in games, we expect to win or lose instead in stories, we expect that the narrative will end a certain way. we will therefore compare two ways for modelling narrative suspense: surprise, a backwardlooking measure of how unexpected the current state is given the story so far; and uncertainty reduction, a forward-looking and measure of how unexpected the continuation of the story is. both measures can be computed either directly over story representations, or indirectly over the probability distributions over such representations. we propose a hierarchical language model based on generative pre-training (gpt, @xcite to encode story-level representations and develop an inference scheme that uses these representations to compute both surprise and uncertainty reduction. for evaluation, we use the writingprompt corpus of short stories @xcite , part of which we annotate with human sentence-by-sentence judgements of suspense. we find that surprise over representations and over probability distributions both predict suspense judgements. however uncertainty reduction over representations is better, resulting in near human-level accuracy. we also show that our models can be used to predict turning points, i.e., major narrative events, in movie synopses @xcite . ## related work in narratology, uncertainty over outcomes is traditionally seen as suspenseful (e.g., @xcite @xcite . other authors claim that suspense can exist without uncertainty (e.g., @xcite @xcite and that readers feel suspense even when they read a story for the second time @xcite , which is unexpected if suspense is uncertainty; this is referred to as the paradox of suspense @xcite . considering romeo and juliet again, in the first view suspense is motivated by primarily by uncertainty over what will happen. who will be hurt or killed in the fight? what will happen after marriage? however, at the beginning of the play we are told "from forth the fatal loins of these two foes, a pair of starcrossed lovers take their life", and so the suspense is more about being invested in the plot than not knowing the outcome, aligning more with the second view: suspense can exist without uncertainty. we do not address the paradox of suspense directly in this paper, but we are guided by the debate to operationalise methods that encompass both views. the hale model is closer to the traditional model of suspense as being about uncertainty. in contrast, the ely model is more in line with the second view that uncertainty matters less than consequentially different outcomes. in nlp, suspense is studied most directly in natural language generation, with systems such as dramatis @xcite and suspenser @xcite , two planning-based story generators that use the theory of @xcite that suspense is created when a protagonist faces obstacles that reduce successful outcomes. our approach, in contrast, models sus-pense using general language models fine-tuned on stories, without planning and domain knowledge. the advantage is that the model can be trained on large volumes of available narrative text without requiring expensive annotations, making it more generalisable. other work emphasises the role of characters and their development in story understanding @xcite @xcite or summarisation @xcite . a further important element of narrative structure is plot, i.e., the sequence of events in which characters interact. neural models have explicitly modelled events @xcite @xcite or the results of actions @xcite . on the other hand, some neural generation models @xcite just use a hierarchical model on top of a language model; our architecture follows this approach. ## conclusions our overall findings suggest that by implementing concepts from psycholinguistic and economic theory, we can predict human judgements of suspense in storytelling. that uncertainty reduction (u ely ) outperforms probability-only (s hale ) and state-only (s ely ) surprise suggests that, while consequential state change is of primary importance for suspense, the probability distribution over the states is also a necessary factor. uncertainty reduction therefore captures the view of suspense as reducing paths to a desired outcome, with more consequential shifts as the story progresses (o'neill and riedl, 2014; @xcite . this is more in line with the @xcite desire-frustration view of suspense, where uncertainty is secondary. strong psycholinguistic claims about suspense are difficult to make due to several weaknesses in our approach, which highlight directions for future research: the proposed model does not have a higher-level understanding of event structure; most likely it picks up the textual cues that accompany dramatic changes in the text. one strand of further work is therefore analysis: text could be artificially manipulated using structural changes, for example by switching the order of sentences, mixing multiple stories, including a summary at the beginning that foreshadows the work, masking key suspenseful words, or paraphrasing. an analogue of this would be adversarial examples used in computer vision. additional annotations, such as how certain readers are about the outcome of the story, may also be helpful in better understanding the relationship between suspense and uncertainty. automated interpretability methods as proposed by @xcite , could shed further light on models' predictions. the recent success of language models in wideranging nlp tasks (e.g., @xcite has shown that language models are capable of learning semantically rich information implicitly. however, generating plausible future continuations is an essential part of the model. in text generation, fan et al. (2019) have found that explicitly incorporating coreference and structured event representations into generation produces more coherent generated text. a more sophisticated model would incorporate similar ideas. autoregressive models that generate step by step alternatives for future continuations are computationally impractical for longer rollouts and are not cognitively plausible. they also differ from the ely et al. (2015) conception of suspense, which is in terms of bayesian beliefs over a longer-term future state, not step by step. there is much recent work (e.g., ha and schmidhuber (2018); gregor et al. ( 2019 )), on state-space approaches that model beliefs as latent states using variational methods. in principle, these would avoid the brute-force calculation of a rollout and conceptually, anticipating longer-term states aligns with theories of suspense. related tasks such as inverting the understanding of suspense to utilise the models in generating more suspenseful stories may also prove fruitful. this paper is a baseline that demonstrates how modern neural network models can implicitly represent text meaning and be useful in a narrative context without recourse to supervision. it provides a springboard to further interesting applications and research on suspense in storytelling.