- The paper proposes a zero-shot video-to-text summarization method utilizing integrated screenplay representations to balance visual and textual modalities.
- It introduces MFACTSUM, a novel metric designed to evaluate multimodal summary quality comprehensively by considering both visual and textual content.
- Evaluation shows the screenplay method captures significantly more visual information with substantially less video input compared to state-of-the-art models.
Integrating Video and Text: A Focused Approach to Multimodal Summary Generation and Evaluation
In the field of Vision-LLMs (VLMs), one recurrent challenge is balancing visual and textual modalities, especially when tasked with summarizing complex multimodal inputs such as entire TV show episodes. In this paper, the authors tackle this issue by proposing a zero-shot video-to-text summarization approach that constructs an integrated screenplay representation of an episode. The innovative screenplay document incorporates key video moments, dialogues, and character information into a unified framework, facilitating a balanced interpretation by both human actors and LLMs.
This approach diverges from previous methods by generating screenplays and naming characters in a zero-shot manner using only the audio, video, and transcripts without additional annotations. A significant contribution of the paper is the introduction of the MFACTSUM metric, designed to evaluate summaries comprehensively concerning vision and text modalities. MFACTSUM addresses the shortcomings of existing metrics that often overlook the visual content, thus providing a balanced framework for evaluating the multimodal fidelity of video-to-text summaries.
Through evaluation on the SummScreen3D dataset, the screenplay summaries exhibited superiority in capturing visual information, showcasing a 20% increase in relevant visual data retention while utilizing 75% less video input compared to state-of-the-art models such as Gemini 1.5 Pro. These results underscore the efficacy of screenplays in enhancing the multimodal richness of summaries.
The implications of this work are multifaceted. Practically, the proposed pipeline offers a cost-effective solution to generate multimodal summaries without extensive video processing, thus improving computational efficiency. Theoretically, the paper sheds light on the inherent biases in current models and evaluation metrics that lean toward textual content, prompting a reconsideration of how multimodal tasks are approached and assessed.
Moving forward, the findings suggest several avenues for future research. Expanding the utility of screenplay-based summaries beyond TV shows to other video domains could enhance modality integration in various applications. Additionally, refining multimodal metrics like MFACTSUM could lead to more nuanced assessments of summary quality. As AI advances, overcoming modality biases remains crucial for developing systems that truly understand and effectively integrate multiple data modalities.
Finally, the financial implications noted indicate that while the proposed methods are effective, they come at a significant computational expense. This raises questions about scalability and accessibility, particularly for smaller organizations or researchers without substantial resources. Addressing these challenges is essential for ensuring that advancements in multimodal summarization are broadly applicable and beneficial.