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MM-AVS: مجموعة بيانات كاملة الحجم لتلخيص متعدد الوسائط
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تصبح التلخصات متعددة الوسائط ذات أهمية متزايدة لأنها هي أساس الإجابة على السؤال والبحث على شبكة الإنترنت والعديد من المهام الأخرى المصب الأخرى.ومع ذلك، فإن موادها التعليمية تفتقر إلى منظمة شاملة من خلال دمج الموارد من الطرائق المختلفة، مما يتخلف عن التقدم المحرز البحثي في هذا المجال.في هذه الدراسة، نطلق عن مجموعة بيانات متعددة الوسائط واسعة النطاق تجمع الوثائق والملخصات والصور والتسميات التوضيحية ومقاطع الفيديو والصوتيات والمنصات واللقب باللغة الإنجليزية من CNN و Daily Mail.لدينا أفضل المعرفة لدينا، هذه هي المجموعة الأولى التي تدعم جميع الطرائق وتشمل ما يقرب من جميع أنواع المواد المتاحة في هذا المجتمع.بالإضافة إلى ذلك، نحن ابتكر نموذج خط الأساس استنادا إلى مجموعة البيانات الجديدة، والذي يستخدم آلية تقفيز تقترح حديثا على أساس النصوص.تتحقق النتائج التجريبية دور المساعدة الهامة للمعلومات الخارجية لتلخيص متعدد الوسائط.
Multimodal summarization becomes increasingly significant as it is the basis for question answering, Web search, and many other downstream tasks. However, its learning materials have been lacking a holistic organization by integrating resources from various modalities, thereby lagging behind the research progress of this field. In this study, we release a full-scale multimodal dataset comprehensively gathering documents, summaries, images, captions, videos, audios, transcripts, and titles in English from CNN and Daily Mail. To our best knowledge, this is the first collection that spans all modalities and nearly comprises all types of materials available in this community. In addition, we devise a baseline model based on the novel dataset, which employs a newly proposed Jump-Attention mechanism based on transcripts. The experimental results validate the important assistance role of the external information for multimodal summarization.
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