Video Summarization: Towards Entity-Aware Captions
(2312.02188)Abstract
Existing popular video captioning benchmarks and models deal with generic captions devoid of specific person, place or organization named entities. In contrast, news videos present a challenging setting where the caption requires such named entities for meaningful summarization. As such, we propose the task of summarizing news video directly to entity-aware captions. We also release a large-scale dataset, VIEWS (VIdeo NEWS), to support research on this task. Further, we propose a method that augments visual information from videos with context retrieved from external world knowledge to generate entity-aware captions. We demonstrate the effectiveness of our approach on three video captioning models. We also show that our approach generalizes to existing news image captions dataset. With all the extensive experiments and insights, we believe we establish a solid basis for future research on this challenging task.
Overview
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The paper introduces a new approach to video summarization that focuses on entity-aware captions, which aims to improve the understanding and indexing of video content, especially news videos.
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A new dataset called VIEWS (VIdeo NEWS) has been introduced for research in entity-focused video captioning, featuring news videos with entity-centric captions.
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The method uses an Entity Perceiver to identify named entities within videos and a Knowledge Retriever to provide context using external knowledge, followed by a sophisticated captioning model.
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The approach has been validated with state-of-the-art video captioning models and has shown to improve caption quality when compared to those relying solely on visual cues.
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Detailed ablation studies suggest further research opportunities, particularly in improving named entity recognition to enhance entity-aware video captioning.
In the expanding field of video understanding, the ability to distill video content into a coherent summary is a valuable asset, not only for assisting users in rapidly consuming relevant portions of lengthy videos but also for enabling more sophisticated video indexing and search functionalities. This significance is particularly pronounced in the context of news videos, where the accurate identification of specific people, places, and organizations—collectively known as named entities—is pivotal for producing meaningful captions that reflect the essence of the video content.
Addressing this challenge, a novel approach to video summarization has been introduced, which puts a spotlight on generating captions that are aware of and incorporate named entities, contrasting the typical generic descriptions seen in most video captioning tasks. To foster research in this specialized summarization task, a new dataset termed VIEWS (VIdeo NEWS) has been released. This dataset is large-scale and features news videos paired with rich, entity-focused captions, validated for high alignment with the video content it describes.
The proposed method takes an innovative approach to video captioning; it first deploys an Entity Perceiver to directly identify named entities from the video. It then leverages a Knowledge Retriever that mines external world knowledge using these detected entities to provide contextual insight. Finally, a cutting-edge captioning model integrates this information, generating entity-aware captions that encapsulate the video's key content.
Rigorous experimentation was carried out to ascertain the efficacy of the proposed approach in enhancing the video captioning task. Using three different state-of-the-art video captioning models, it was shown that integrating external knowledge and entity recognition substantially improves the quality of the generated video captions in comparison to models that rely solely on visual information. Furthermore, when applied to an existing news image-captioning dataset, the proposed approach demonstrated impressive generalization capabilities, indicating its potential adaptability across various types of textual-visual content.
The insights gained from a series of detailed ablation studies point to interesting opportunities for future research. For instance, improving the recognition of named entities has the potential to markedly amplify the performance gains in entity-aware video captioning. As such, this study establishes a robust foundation for ongoing exploration in the realm of entity-aware video summarization, presenting exciting avenues for the development of more nuanced and informative video captioning solutions.
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