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Register a new userEfficient Mind-Map Generation via Sequence-to-Graph and Reinforced Graph Refinement
جيل خريطة العقل الفعال عبر التسلسل إلى الرسم البياني وتحويل الرسم البياني المقوى
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خريطة العقل هي رسم تخطيطي يمثل المفهوم المركزي والأفكار الرئيسية بطريقة هرمية. سيؤدي تحويل النص العادي إلى خريطة ذهنية إلى الكشف عن هيكلها الدلالي الرئيسي وتكون أسهل في فهمه. بالنظر إلى وثيقة، تستخرج طريقة جيل خريطة العقل التلقائي الحالية علاقات كل زوج جملة لتوليد الرسم البياني الدلالي الموجه لهذا المستند. تزداد تعقيد الحساب بشكل كبير مع طول الوثيقة. علاوة على ذلك، من الصعب التقاط الدلالات الإجمالية. للتعامل مع التحديات المذكورة أعلاه، نقترح شبكة جيل مخريطة العقل الفعالة تقوم بتحويل وثيقة إلى رسم بياني عبر الرسوم البيانية بالتسلسل إلى الرسم البياني. لضمان خريطة ذهنية ذات مغزى، نقوم بتصميم وحدة تحويل رسم بياني لضبط الرسم البياني العلاقة بطريقة تعليمية للتعزيز. تظهر النتائج التجريبية الواسعة أن النهج المقترح أكثر فعالية وكفاءة من الأساليب الحالية. يتم تقليل وقت الاستدلال بآلاف المرات مقارنة بالطرق الحالية. تتحقق دراسات الحالة أن خرائط العقل التي تم إنشاؤها بشكل أفضل تكشف عن الهياكل الدلالية الأساسية للوثيقة.
A mind-map is a diagram that represents the central concept and key ideas in a hierarchical way. Converting plain text into a mind-map will reveal its key semantic structure and be easier to understand. Given a document, the existing automatic mind-map generation method extracts the relationships of every sentence pair to generate the directed semantic graph for this document. The computation complexity increases exponentially with the length of the document. Moreover, it is difficult to capture the overall semantics. To deal with the above challenges, we propose an efficient mind-map generation network that converts a document into a graph via sequence-to-graph. To guarantee a meaningful mind-map, we design a graph refinement module to adjust the relation graph in a reinforcement learning manner. Extensive experimental results demonstrate that the proposed approach is more effective and efficient than the existing methods. The inference time is reduced by thousands of times compared with the existing methods. The case studies verify that the generated mind-maps better reveal the underlying semantic structures of the document.
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