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Register a new userImproving Abstractive Summarization with Commonsense Knowledge
تحسين تلخيص الجماعي مع معرفة المنطقية
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أظهرت نماذج واسعة النطاق على نطاق واسع عروضا قوية على العديد من توليد اللغة الطبيعية وفهم المعايير.ومع ذلك، فإن إدخال العمولة فيها لتوليد نص أكثر واقعية يظل تحديا.مستوحاة من العمل السابق على جيل المعرفة المنطقي ومنطق العموم التوليد، نقدم طريقتين لإضافة مهارات ومعرفة المنطق المنطقي إلى نماذج تلخيص مبادرة.فازت هذه الطريقة على خط الأساس على درجات الحمر، مما يدل على تفوق نماذجنا على أساس الأساس.تشير نتائج التقييم البشري إلى أن الملخصات الناتجة عن طريقتنا أكثر واقعية ولديها أخطاء معدلة أقل.
Large scale pretrained models have demonstrated strong performances on several natural language generation and understanding benchmarks. However, introducing commonsense into them to generate more realistic text remains a challenge. Inspired from previous work on commonsense knowledge generation and generative commonsense reasoning, we introduce two methods to add commonsense reasoning skills and knowledge into abstractive summarization models. Both methods beat the baseline on ROUGE scores, demonstrating the superiority of our models over the baseline. Human evaluation results suggest that summaries generated by our methods are more realistic and have fewer commonsensical errors.
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