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Register a new userHETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization
Hetformer: محول غير متجانس مع انتباه متناثر لتلخيص الاستخراج طويل النص
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لالتقاط بنية الرسم البياني الدلالي من النص الخام، يتم بناء معظم طرق التلخيص الموجودة على GNNS مع نموذج مدرب مسبقا.ومع ذلك، فإن هذه الأساليب تعاني من إجراءات مرهقة وحسابات غير فعالة وثائق نصية طويلة.لتخفيف هذه المشكلات، تقترح هذه الورقة HETFORMER، وهو نموذج مدرب مسبقا من المحولات مع انتباه متفرج متعدد الحبيبات لتلخيص الاستخراجي لفترة طويلة.على وجه التحديد، نقوم بالنماذج أنواع مختلفة من العقد الدلالية في النص الخام كشركة بيانية غير متجانسة محتملة وتعلم العلاقات المخللة بشكل مباشر (حواف) بين العقد بواسطة المحول.تظهر تجارب واسعة النطاق على كل من مهام تلخيص مستندات واحدة ومتعددة المستندات أن HETFORMER تحقق أداء حديثة في Rouge F1 أثناء استخدام ذاكرة أقل ومعلمات أقل.
To capture the semantic graph structure from raw text, most existing summarization approaches are built on GNNs with a pre-trained model. However, these methods suffer from cumbersome procedures and inefficient computations for long-text documents. To mitigate these issues, this paper proposes HetFormer, a Transformer-based pre-trained model with multi-granularity sparse attentions for long-text extractive summarization. Specifically, we model different types of semantic nodes in raw text as a potential heterogeneous graph and directly learn heterogeneous relationships (edges) among nodes by Transformer. Extensive experiments on both single- and multi-document summarization tasks show that HetFormer achieves state-of-the-art performance in Rouge F1 while using less memory and fewer parameters.
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