Automated storytelling has long captured the attention of researchers for the ubiquity of narratives in everyday life. However, it is challenging to maintain coherence and stay on-topic toward a specific ending when generating narratives with neural language models. In this paper, we introduce Story generation with Reader Models (StoRM), a framework in which a reader model is used to reason about the story should progress. A reader model infers what a human reader believes about the concepts, entities, and relations about the fictional story world. We show how an explicit reader model represented as a knowledge graph affords story coherence and provides controllability in the form of achieving a given story world state goal. Experiments show that our model produces significantly more coherent and on-topic stories, outperforming baselines in dimensions including plot plausibility and staying on topic.
We explore the correlation between the sentiment arcs of H. C. Andersen's fairy tales and their popularity, measured as their average score on the platform GoodReads. Specifically, we do not conceive a story's overall sentimental trend as predictive \textit{per se}, but we focus on its coherence and predictability over time as represented by the arc's Hurst exponent. We find that degrading Hurst values tend to imply degrading quality scores, while a Hurst exponent between .55 and .65 might indicate a "sweet spot" for literary appreciation.
StoryLens: Preference-Aligned Story Rewriting via Context-Aware Narrative Enrichment
Beijing University of Posts and Telecommunications（北京邮电大学）；AIM3 Lab, Renmin University of China（中国人民大学AIM3实验室）；Nanyang Technological University（南洋理工大学）
The Challenge and Reward of Fair Play in Narrative: A Computational Approach
Department of Computer Science Hebrew University of Jerusalem（计算机科学系 Hebrew University of Jerusalem）；Department of Law and Digital Humanities Hebrew University of Jerusalem（法律与数字人文系 Hebrew University of Jerusalem）
Automated story generation remains a difficult area of research because it lacks strong objective measures. Generated stories may be linguistically sound, but in many cases suffer poor narrative coherence required for a compelling, logically-sound story. To address this, we present Fabula Entropy Indexing (FEI), an evaluation method to assess story coherence by measuring the degree to which human participants agree with each other when answering true/false questions about stories. We devise two theoretically grounded measures of reader question-answering entropy, the entropy of world coherence…
Georgia Tech（佐治亚理工学院）；EleutherAI；University of Utah（犹他大学）；Carleton University（卡尔顿大学）
In this paper, we propose the beginnings of a formal framework for modeling narrative \textit{qua} narrative. Our framework affords the ability to discuss key qualities of stories and their communication, including the flow of information from a Narrator to a Reader, the evolution of a Reader's story model over time, and Reader uncertainty. We demonstrate its applicability to computational narratology by giving explicit algorithms for measuring the accuracy with which information was conveyed to the Reader and two novel measurements of story coherence.
Social Story Frames: Contextual Reasoning about Narrative Intent and Reception
Carnegie Mellon University（卡内基梅隆大学）；University of Colorado Boulder（科罗拉多大学博尔德分校）；University of Michigan-Flint（密歇根大学弗林特分校）；University of Connecticut（康涅狄格大学）；McGill University（麦吉尔大学）
University of California, Santa Cruz（加州大学圣克鲁兹分校）；University of Chicago（芝加哥大学）；Allen Institute for AI（艾伦人工智能研究院）；UNC Chapel Hill（北卡罗来纳大学教堂山分校）；ETH Zurich（苏黎世联邦理工学院）
When reading a literary piece, readers often make inferences about various characters' roles, personalities, relationships, intents, actions, etc. While humans can readily draw upon their past experiences to build such a character-centric view of the narrative, understanding characters in narratives can be a challenging task for machines. To encourage research in this field of character-centric narrative understanding, we present LiSCU -- a new dataset of literary pieces and their summaries paired with descriptions of characters that appear in them. We also introduce two new tasks on LiSCU: Ch…
Automated plot generation is the challenge of generating a sequence of events that will be perceived by readers as the plot of a coherent story. Traditional symbolic planners plan a story from a goal state and guarantee logical causal plot coherence but rely on a library of hand-crafted actions with their preconditions and effects. This closed world setting limits the length and diversity of what symbolic planners can generate. On the other hand, pre-trained neural language models can generate stories with great diversity, while being generally incapable of ending a story in a specified manner…
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Storium；University of California Los Angeles（加利福尼亚大学洛杉矶分校）
Systems for story generation are asked to produce plausible and enjoyable stories given an input context. This task is underspecified, as a vast number of diverse stories can originate from a single input. The large output space makes it difficult to build and evaluate story generation models, as (1) existing datasets lack rich enough contexts to meaningfully guide models, and (2) existing evaluations (both crowdsourced and automatic) are unreliable for assessing long-form creative text. To address these issues, we introduce a dataset and evaluation platform built from STORIUM, an online colla…
Image narrative generation is a task to create a story from an image with a subjective viewpoint. Given the importance of the subjective feelings of writers, readers, and characters in storytelling, an image narrative generation method should consider human emotion. In this study, we propose a novel method of image narrative generation called ViNTER (Visual Narrative Transformer with Emotion arc Representation), which takes "emotion arc" as input to capture a sequence of emotional changes. Since emotion arcs represent the trajectory of emotional change, it is expected that we can include detai…
Reader reviews of literary fiction on social media, especially those in persistent, dedicated forums, create and are in turn driven by underlying narrative frameworks. In their comments about a novel, readers generally include only a subset of characters and their relationships, thus offering a limited perspective on that work. Yet in aggregate, these reviews capture an underlying narrative framework comprised of different actants (people, places, things), their roles, and interactions that we label the "consensus narrative framework". We represent this framework in the form of an actant-relat…
Reframe Your Life Story: Interactive Narrative Therapist and Innovative Moment Assessment with Large Language Models
Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence（北京交通大数据挖掘与具身智能重点实验室）；Beijing Jiaotong University（北京交通大学）；CoAI Group（CoAI集团）；DCST（国防科技大学）；IAI（人工智能院）；BNRIST（北航机器人与智能技术研究院）；Tsinghua University（清华大学）；Tencent Jarvis Lab（腾讯Jarvis实验室）；Singapore University of Technology and Design（新加坡科技与设计大学）；Central South University（中南大学）；Lingxin AI（灵犀AI）
Institute for Language, Cognition and Computation（语言、认知与计算研究所）；School of Informatics, University of Edinburgh（爱丁堡大学信息学院）
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 t…
Université de Montréal（蒙特利尔大学）；Microsoft XiaoIce（微软小冰）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Beijing Film Academy（北京电影学院）
It is appealing to have a system that generates a story or scripts automatically from a story-line, even though this is still out of our reach. In dialogue systems, it would also be useful to drive dialogues by a dialogue plan. In this paper, we address a key problem involved in these applications -- guiding a dialogue by a narrative. The proposed model ScriptWriter selects the best response among the candidates that fit the context as well as the given narrative. It keeps track of what in the narrative has been said and what is to be said. A narrative plays a different role than the context (…
XL-HeadTags: Leveraging Multimodal Retrieval Augmentation for the Multilingual Generation of News Headlines and Tags
Ahsanullah University of Science & Technology（阿萨努拉科技大学）；University of Alberta（阿尔伯塔大学）；Université de Sherbrooke（谢布鲁克大学）；Salesforce Research（Salesforce研究院）；Nanyang Technological University（南洋理工大学）
Harvard SEAS（哈佛大学工程与应用科学学院）；Adobe Research（奥多比研究院）；University of Illinois Urbana Champaign（伊利诺伊大学厄巴纳-香槟分校）
Titles of short sections within long documents support readers by guiding their focus towards relevant passages and by providing anchor-points that help to understand the progression of the document. The positive effects of section titles are even more pronounced when measured on readers with less developed reading abilities, for example in communities with limited labeled text resources. We, therefore, aim to develop techniques to generate section titles in low-resource environments. In particular, we present an extractive pipeline for section title generation by first selecting the most sali…
EvolvTrip: Enhancing Literary Character Understanding with Temporal Theory-of-Mind Graphs
The University of Manchester（曼彻斯特大学）；King’s College London（伦敦国王学院）；Huawei London Research Centre（华为伦敦研究中心）；Huawei Technologies Co., Ltd.（华为技术有限公司）；The Alan Turing Institute（艾伦·图灵研究所）
