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Register a new userExtractive Research Slide Generation Using Windowed Labeling Ranking
جيل شريطي البحوث الاستخراجية باستخدام تصنيف وضع العلامات النوافذ
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توفر شرائح العرض التقديمية الناتجة عن أوراق البحث الأصلية نموذجا فعالا لتقديم ابتكارات بحثية.توليد شرائح العرض يدويا هي كثيفة العمالة.نقترح طريقة لإنشاء الشرائح تلقائيا للمقالات العلمية المستندة إلى كائن من 5000 من أزواج الشريحة الورقية التي تم تجميعها من مواقع ويب إجراءات المؤتمرات.تستند وحدة وضع العلامات الخاصة بالحكم من طريقتنا إلى SummarUnner، نموذج التسلسل العصبي لتلخيص الاستخراج.بدلا من الترتيب الجمل بناء على أوجه التشابه الدلالي في المستند بأكمله، تقيس خوارزميةنا أهمية وحد الجمل عن طريق الجمع بين الميزات الدلالية والجنيات في إطار الجملة.تتفوق طريقةنا على العديد من الطرق الأساسية بما في ذلك SummarUnner بهامش مهم من حيث درجة Rouge.
Presentation slides generated from original research papers provide an efficient form to present research innovations. Manually generating presentation slides is labor-intensive. We propose a method to automatically generates slides for scientific articles based on a corpus of 5000 paper-slide pairs compiled from conference proceedings websites. The sentence labeling module of our method is based on SummaRuNNer, a neural sequence model for extractive summarization. Instead of ranking sentences based on semantic similarities in the whole document, our algorithm measures the importance and novelty of sentences by combining semantic and lexical features within a sentence window. Our method outperforms several baseline methods including SummaRuNNer by a significant margin in terms of ROUGE score.
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