5 accepted papers
Existing multimodal summarization approaches struggle with scenarios involving numerous images as input, leading to a heavy load for readers. Summarizing both the input text and numerous images helps readers quickly grasp the key points of multimodal input. This paper introduces a novel task, Numero…
LLMs have achieved remarkable fluency and coherence in text generation, yet their widespread adoption has raised concerns about content reliability and accountability. In high-stakes domains, it is crucial to understand where and how the content is created. To address this, we introduce the Text pRO…
Existing multimodal summarization approaches focus on fusing image features in the encoding process, ignoring the individualized needs for images when generating different summaries. However, whether intuitively or empirically, not all images can improve summary quality. Therefore, we propose a nove…
Multimodal summarization usually suffers from the problem that the contribution of the visual modality is unclear. Existing multimodal summarization approaches focus on designing the fusion methods of different modalities, while ignoring the adaptive conditions under which visual modalities are usef…
Multi-document summarization is gaining more and more attention recently and serves as an invaluable tool to obtain key facts among a large information pool. In this paper, we proposed a multi-document hybrid summarization approach, which simultaneously generates a human-readable summary and extract…