TLDW: Extreme Multimodal Summarisation of News Videos

• URL: http://arxiv.org/abs/2210.08481v1
• Date: Sun, 16 Oct 2022 08:19:59 GMT
• Title: TLDW: Extreme Multimodal Summarisation of News Videos
• Authors: Peggy Tang, Kun Hu, Lei Zhang, Jiebo Luo, Zhiyong Wang
• Abstract summary: We introduce eXtreme Multimodal Summarisation with Multimodal Output (XMSMO) for the scenario of TL;DW - Too Long; Didn't Watch, akin to TL;DR. XMSMO aims to summarise a video-document pair into a summary with an extremely short length, which consists of one cover frame as the visual summary and one sentence as the textual summary. Our method is trained, without using reference summaries, by optimising the visual and textual coverage from the perspectives of the distance between the semantic distributions under optimal transport plans.
• Score: 76.50305095899958
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal summarisation with multimodal output is drawing increasing attention due to the rapid growth of multimedia data. While several methods have been proposed to summarise visual-text contents, their multimodal outputs are not succinct enough at an extreme level to address the information overload issue. To the end of extreme multimodal summarisation, we introduce a new task, eXtreme Multimodal Summarisation with Multimodal Output (XMSMO) for the scenario of TL;DW - Too Long; Didn't Watch, akin to TL;DR. XMSMO aims to summarise a video-document pair into a summary with an extremely short length, which consists of one cover frame as the visual summary and one sentence as the textual summary. We propose a novel unsupervised Hierarchical Optimal Transport Network (HOT-Net) consisting of three components: hierarchical multimodal encoders, hierarchical multimodal fusion decoders, and optimal transport solvers. Our method is trained, without using reference summaries, by optimising the visual and textual coverage from the perspectives of the distance between the semantic distributions under optimal transport plans. To facilitate the study on this task, we collect a large-scale dataset XMSMO-News by harvesting 4,891 video-document pairs. The experimental results show that our method achieves promising performance in terms of ROUGE and IoU metrics.

Related papers

• Multimodality Helps Few-Shot 3D Point Cloud Semantic Segmentation [61.91492500828508]
  Few-shot 3D point cloud segmentation (FS-PCS) aims at generalizing models to segment novel categories with minimal support samples. We introduce a cost-free multimodal FS-PCS setup, utilizing textual labels and the potentially available 2D image modality. We propose a simple yet effective Test-time Adaptive Cross-modal Seg (TACC) technique to mitigate training bias.
  arXiv  Detail & Related papers  (2024-10-29T19:28:41Z)
• Align and Attend: Multimodal Summarization with Dual Contrastive Losses [57.83012574678091]
  The goal of multimodal summarization is to extract the most important information from different modalities to form output summaries. Existing methods fail to leverage the temporal correspondence between different modalities and ignore the intrinsic correlation between different samples. We introduce Align and Attend Multimodal Summarization (A2Summ), a unified multimodal transformer-based model which can effectively align and attend the multimodal input.
  arXiv  Detail & Related papers  (2023-03-13T17:01:42Z)
• CISum: Learning Cross-modality Interaction to Enhance Multimodal Semantic Coverage for Multimodal Summarization [2.461695698601437]
  This paper proposes a multi-task cross-modality learning framework (CISum) to improve multimodal semantic coverage. To obtain the visual semantics, we translate images into visual descriptions based on the correlation with text content. Then, the visual description and text content are fused to generate the textual summary to capture the semantics of the multimodal content.
  arXiv  Detail & Related papers  (2023-02-20T11:57:23Z)
• Hierarchical3D Adapters for Long Video-to-text Summarization [79.01926022762093]
  multimodal information offers superior performance over more memory-heavy and fully fine-tuned textual summarization methods. Our experiments demonstrate that multimodal information offers superior performance over more memory-heavy and fully fine-tuned textual summarization methods.
  arXiv  Detail & Related papers  (2022-10-10T16:44:36Z)
• Semantics-Consistent Cross-domain Summarization via Optimal Transport Alignment [80.18786847090522]
  We propose a Semantics-Consistent Cross-domain Summarization model based on optimal transport alignment with visual and textual segmentation. We evaluated our method on three recent multimodal datasets and demonstrated the effectiveness of our method in producing high-quality multimodal summaries.
  arXiv  Detail & Related papers  (2022-10-10T14:27:10Z)
• MHMS: Multimodal Hierarchical Multimedia Summarization [80.18786847090522]
  We propose a multimodal hierarchical multimedia summarization (MHMS) framework by interacting visual and language domains. Our method contains video and textual segmentation and summarization module, respectively. It formulates a cross-domain alignment objective with optimal transport distance to generate the representative and textual summary.
  arXiv  Detail & Related papers  (2022-04-07T21:00:40Z)
• See, Hear, Read: Leveraging Multimodality with Guided Attention for Abstractive Text Summarization [14.881597737762316]
  We introduce the first large-scale dataset for abstractive text summarization with videos of diverse duration, compiled from presentations in well-known academic conferences like NDSS, ICML, NeurIPS, etc. We then propose name, a factorized multi-modal Transformer based decoder-only language model, which inherently captures the intra-modal and inter-modal dynamics within various input modalities for the text summarization task.
  arXiv  Detail & Related papers  (2021-05-20T08:56:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.