Yue Hu
Publications
S^2tory: Story Spine Distillation for Movie Script Summarization
Movie scripts pose a fundamental challenge for automatic summarization due to their non-linear, cross-cut narrative structure, which makes surface-level saliency methods ineffective at preserving core story progression. To address this, we introduce S^2tory (Story Spine Distillation), a narratology-grounded framework that leverages character development trajectories to identify plot nuclei, the essential events that drive the narrative forward, while filtering out peripheral satellite events that merely enrich atmosphere or emotion. Our Narrative Expert Agent (NEAgent) performs theory-constrained reasoning, whose distilled knowledge conditions a small model to identify plot nuclei. Another model then uses these plot nuclei to generate the summary. Experiments on the MovieSum dataset demonstrate state-of-the-art semantic fidelity at approximately 3.5x compression, and zero-shot evaluation on BookSum confirms strong out-of-domain generalization. Human evaluation further validates that narratological theory provides an indispensable foundation for modeling complex, non-linear narratives.
MemGovern: Enhancing Code Agents through Learning from Governed Human Experiences
While autonomous software engineering (SWE) agents are reshaping programming paradigms, they currently suffer from a "closed-world" limitation: they attempt to fix bugs from scratch or solely using local context, ignoring the immense historical human experience available on platforms like GitHub. Accessing this open-world experience is hindered by the unstructured and fragmented nature of real-world issue-tracking data. In this paper, we introduce MemGovern, a framework designed to govern and transform raw GitHub data into actionable experiential memory for agents. MemGovern employs experience governance to convert human experience into agent-friendly experience cards and introduces an agentic experience search strategy that enables logic-driven retrieval of human expertise. By producing 135K governed experience cards, MemGovern achieves a significant performance boost, improving resolution rates on the SWE-bench Verified by 4.65%. As a plug-in approach, MemGovern provides a solution for agent-friendly memory infrastructure.
LitVISTA: A Benchmark for Narrative Orchestration in Literary Text
Computational narrative analysis aims to capture rhythm, tension, and emotional dynamics in literary texts. Existing large language models can generate long stories but overly focus on causal coherence, neglecting the complex story arcs and orchestration inherent in human narratives. This creates a structural misalignment between model- and human-generated narratives. We propose VISTA Space, a high-dimensional representational framework for narrative orchestration that unifies human and model narrative perspectives. We further introduce LitVISTA, a structurally annotated benchmark grounded in literary texts, enabling systematic evaluation of models' narrative orchestration capabilities. We conduct oracle evaluations on a diverse selection of frontier LLMs, including GPT, Claude, Grok, and Gemini. Results reveal systematic deficiencies: existing models fail to construct a unified global narrative view, struggling to jointly capture narrative function and structure. Furthermore, even advanced thinking modes yield only limited gains for such literary narrative understanding.