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1. Salience Vectors for Measuring Distance Between Stories

   Farrell, Rachelyn; Fisher, Mira; Ware, Stephen G. (October 2022, Proceedings of the 18th AAAI international conference on Artificial Intelligence and Interactive Digital Entertainment)

   Narrative planners generate sequences of actions that represent story plots given a story domain model. This is a useful way to create branching stories for interactive narrative systems that maintain logical consistency across multiple storylines with different content. There is a need for story comparison techniques that can enable systems like experience managers and domain authoring tools to reason about similarities and differences between multiple stories or branches. We present an algorithm for summarizing narrative plans as numeric vectors based on a cognitive model of human story perception. The vectors encode important story information and can be compared using standard distance functions to quantify the overall semantic difference between two stories. We show that this distance metric is highly accurate based on human annotations of story similarity, and compare it to several alternative approaches. We also explore variations of our method in an attempt to broaden its applicability to other types of story systems. 

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2. Salience Allocation as Guidance for Abstractive Summarization

   Wang, Fei; Song, Kaiqiang; Zhang, Hongming; Jin, Lifeng; Cho, Sangwoo; Yao, Wenlin; Wang, Xiaoyang; Chen, Muhao; Yu, Dong (January 2022, Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing)

   Abstractive summarization models typically learn to capture the salient information from scratch implicitly.Recent literature adds extractive summaries as guidance for abstractive summarization models to provide hints of salient content and achieves better performance.However, extractive summaries as guidance could be over strict, leading to information loss or noisy signals.Furthermore, it cannot easily adapt to documents with various abstractiveness.As the number and allocation of salience content pieces varies, it is hard to find a fixed threshold deciding which content should be included in the guidance.In this paper, we propose a novel summarization approach with a flexible and reliable salience guidance, namely SEASON (SaliencE Allocation as Guidance for Abstractive SummarizatiON).SEASON utilizes the allocation of salience expectation to guide abstractive summarization and adapts well to articles in different abstractiveness.Automatic and human evaluations on two benchmark datasets show that the proposed method is effective and reliable.Empirical results on more than one million news articles demonstrate a natural fifteen-fifty salience split for news article sentences, providing a useful insight for composing news articles. 

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3. Table-based Fact Verification With Salience-aware Learning

   https://doi.org/10.18653/v1/2021.findings-emnlp.338  

   Wang, Fei; Sun, Kexuan; Pujara, Jay; Szekely, Pedro; Chen, Muhao (January 2021, Findings of the Association for Computational Linguistics: EMNLP 2021)

   Tables provide valuable knowledge that can be used to verify textual statements. While a number of works have considered table-based fact verification, direct alignments of tabular data with tokens in textual statements are rarely available. Moreover, training a generalized fact verification model requires abundant labeled training data. In this paper, we propose a novel system to address these problems. Inspired by counterfactual causality, our system identifies token-level salience in the statement with probing-based salience estimation. Salience estimation allows enhanced learning of fact verification from two perspectives. From one perspective, our system conducts masked salient token prediction to enhance the model for alignment and reasoning between the table and the statement. From the other perspective, our system applies salience-aware data augmentation to generate a more diverse set of training instances by replacing non-salient terms. Experimental results on TabFact show the effective improvement by the proposed salience-aware learning techniques, leading to the new SOTA performance on the benchmark. 

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4. Salience as a Narrative Planning Step Cost Function

   https://doi.org/10.1109/CoG51982.2022.9893667  

   Ware, Stephen G.; Farrell, Rachelyn (August 2022, Proceedings of the 2022 IEEE Conference on Games)

   Psychological research has demonstrated that as we experience a story several features affect the salience of its events in memory. These features correspond to who? where? when? how? and why? questions about those events. Computational models of salience have been used in interactive narratives to measure which events people most easily remember from the past and which they expect more readily from the future. We use three example domains to show that events in sequences that are solutions to narrative planning problems are generally more salient with each other, and events in non-solution sequences are less salient with each other. This means that measuring the salience of a sequence of actions during planning can serve as an efficient cost function to improve the speed, and perhaps also the quality, of a narrative planner. 

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5. ConFUSE: Confusion-based Federated Unlearning with Salience Exploration

   Meerza, Syed_Irfan_Al; Sadovnik, Ami; Liu, Jian (July 2024, Proceedings of the IEEE Computer Society Annual Symposium on VLSI {ISVLSI-24})