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Mitigating Data Scarceness through Data Synthesis, Augmentation and Curriculum for Abstractive Summarization

التخفيف من قطع البيانات من خلال تخليق البيانات، والتكبير والمناهج الدراسية لتلخيص التغذية

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 Publication date 2021
and research's language is English
 Created by Shamra Editor




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This paper explores three simple data manipulation techniques (synthesis, augmentation, curriculum) for improving abstractive summarization models without the need for any additional data. We introduce a method of data synthesis with paraphrasing, a data augmentation technique with sample mixing, and curriculum learning with two new difficulty metrics based on specificity and abstractiveness. We conduct experiments to show that these three techniques can help improve abstractive summarization across two summarization models and two different small datasets. Furthermore, we show that these techniques can improve performance when applied in isolation and when combined.



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تعرض المحاضرة شرح عن علم البيانات وعلاقته بعلم الإحصاء والتعلم الآلي وحالتين دراسيتين عن دور عالم البيانات في تصميم حلول تعتمد على استخراج المعرفة من حجم كبير من البيانات المتوفرة, كما يتم عرض أهم المهام في المؤتمرات العلمية التي يمكن المشاركة بها لطلاب المعلوماتية المهتمين بهذا المجال
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