基于多目标优化的多模态摘要生成
Multi-Modal Summary Generation using Multi-Objective Optimization
摘要：
近年来，通信技术的发展推动了多模态摘要技术的研究。以前的大多数多模态摘要工作都集中在文本和图像上。在本文中，我们提出了一种新的提取多目标优化模型，以产生一个包含文本，图像和视频的多模态摘要。在多目标优化框架中同时优化重要目标，如模态内显著性，跨模态冗余和跨模态相似性，以产生有效的多模态输出。所提出的模型已被单独评估为不同的方式，并已被发现比国家的最先进的方法更好地执行。
英文摘要：
Significant development of communication technology over the past few years has motivated research in multi-modal summarization techniques. A majority of the previous works on multi-modal summarization focus on text and images. In this paper, we propose a novel extractive multi-objective optimization based model to produce a multi-modal summary containing text, images, and videos. Important objectives such as intra-modality salience, cross-modal redundancy and cross-modal similarity are optimized simultaneously in a multi-objective optimization framework to produce effective multi-modal output. The proposed model has been evaluated separately for different modalities, and has been found to perform better than state-of-the-art approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
