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Register a new userNutri-bullets Hybrid: Consensual Multi-document Summarization
Nutri-Bullets Hybrid: تلخيص توثيق متعدد الوثائق
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نقدم طريقة لتوليد ملخصات مقارنة تسليط الضوء على أوجه التشابه والتناقضات في وثائق المدخلات. التحدي الرئيسي في إنشاء هذه الملخصات هو عدم وجود بيانات تدريبية متوازية كبيرة مطلوبة لتدريب أنظمة التلخيص النموذجية. تحقيقا لهذه الغاية، نقدم نهج جيل مختلفي مستوحى من أنظمة المفاهيم التقليدية إلى النص. لتمكين المقارنة الدقيقة بين المصادر المختلفة، يتعلم النموذج أولا استخراج العلاقات ذات الصلة من وثائق المدخلات. يستخدم مكون تخطيط المحتوى المشغلين المحددين لتجميع هذه العلاقات بعد تحديد مجموعة فرعية للإدماج في ملخص. مكون إدراك السطح Lexicalizes هذه المعلومات باستخدام نموذج لغة تسقط النص. من خلال اختيار محتوى النمذجة بشكل منفصل وإدراكه، يمكننا تدريبها بشكل فعال مع التعليقات التوضيحية المحدودة. نفذنا واختبرنا النموذج في مجال التغذية والصحة - تنتشر بالتناسيل. مقارنة بالأساليب التقليدية، يؤدي إطارنا إلى تلخيص أكثر مخلصة وثيقة ومهمة الحساسة للتجميع - بينما يجري بطلاقة بنفس القدر.
We present a method for generating comparative summaries that highlight similarities and contradictions in input documents. The key challenge in creating such summaries is the lack of large parallel training data required for training typical summarization systems. To this end, we introduce a hybrid generation approach inspired by traditional concept-to-text systems. To enable accurate comparison between different sources, the model first learns to extract pertinent relations from input documents. The content planning component uses deterministic operators to aggregate these relations after identifying a subset for inclusion into a summary. The surface realization component lexicalizes this information using a text-infilling language model. By separately modeling content selection and realization, we can effectively train them with limited annotations. We implemented and tested the model in the domain of nutrition and health -- rife with inconsistencies. Compared to conventional methods, our framework leads to more faithful, relevant and aggregation-sensitive summarization -- while being equally fluent.
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