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Streamhover: ملخص ترتيب Livestream والشروح
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مع النمو المتفجر للبث LivestReam، هناك حاجة ملحة لتكنولوجيا التلخيص الجديدة التي تمكننا من إنشاء معاينة للمحتوى البثاري والاستفادة من هذه الثروة من المعرفة. ومع ذلك، فإن المشكلة غير صاخبة بسبب الطبيعة غير الرسمية للغة المنطوقة. علاوة على ذلك، كان هناك نقص في مجموعات البيانات المشروح اللازمة لتلخيص النص. في هذه الورقة، نقدم Streamhover، إطارا للتعليق ويلخص النصوص Livestream. مع وجود ما مجموعه أكثر من 500 ساعة من مقاطع الفيديو الموحدة مع كل من ملخصات الاستخراجية والمخفية، فإن مجموعة بياناتنا القياسية أكبر بكثير من شرائح مشروح حاليا. نستكشف نموذج تلخيص الاستخراج العصبي الذي يهدف إلى أن يقوم AutoNcoder بالتالي من Vector-Vector بتكنولوجيا المعلومات لتعلم تمثيلات ناقلات كامنة للكلمات المنطوقة وتحديد الكلام البارزين من النصوص لتشكيل ملخصات. نظهر أن نموذجنا تعميم أفضل ويحسن الأداء على خطوط الأساس القوية. توفر نتائج هذه الدراسة وسيلة للبحث في المستقبل لتحسين حلول تلخيص للتصفح الفعال للمهارات.
With the explosive growth of livestream broadcasting, there is an urgent need for new summarization technology that enables us to create a preview of streamed content and tap into this wealth of knowledge. However, the problem is nontrivial due to the informal nature of spoken language. Further, there has been a shortage of annotated datasets that are necessary for transcript summarization. In this paper, we present StreamHover, a framework for annotating and summarizing livestream transcripts. With a total of over 500 hours of videos annotated with both extractive and abstractive summaries, our benchmark dataset is significantly larger than currently existing annotated corpora. We explore a neural extractive summarization model that leverages vector-quantized variational autoencoder to learn latent vector representations of spoken utterances and identify salient utterances from the transcripts to form summaries. We show that our model generalizes better and improves performance over strong baselines. The results of this study provide an avenue for future research to improve summarization solutions for efficient browsing of livestreams.
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