Despite recent advancements in neural retrieval, representing text fragments or phrases with proper contextualized embeddings is still challenging. Particularly in video retrieval, where documents are text extracted through OCR from the frames or ASR from audio tracks, the textual content is rarely complete sentences but only a bag of phrases. In this work, we propose FORTIFY, a generative model fine-tuning approach for noisy document rewriting and summarization, to improve the downstream retrieval effectiveness. By experimenting on MultiVENT 2.0, an informational video retrieval benchmark, we show Llama fine-tuned with FORTIFY provides an effective document expansion, leading to a 30% improvement over prompting an out-of-box Llama model on nDCG@10. Zero-shot transferring the model tailored for MultiVENT 2.0 to two out-of-distribution datasets still demonstrates competitive retrieval effectiveness to other document preprocessing alternatives.

How are we able to learn about complex current events just from short snippets of video? While natural language enables straightforward ways to represent under-specified, partially observable events, visual data does not facilitate analogous methods and, consequently, introduces unique challenges in event understanding. With the growing prevalence of vision-capable AI agents, these systems must be able to model events from collections of unstructured video data. To tackle robust event modeling in multimodal settings, we introduce a multimodal formulation for partially-defined events and cast the extraction of these events as a three-stage span retrieval task. We propose a corresponding benchmark for this task, MultiVENT-G, that consists of 14.5 hours of densely annotated current event videos and 1,168 text documents, containing 22.8K labeled event-centric entities. We propose a collection of LLM-driven approaches to the task of multimodal event analysis, and evaluate them on MultiVENT-G. Results illustrate the challenges that abstract event understanding poses and demonstrates promise in event-centric video-language systems.