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Register a new userEnhancing Factual Consistency of Abstractive Summarization
تعزيز الاتساق الواقعي لتلخيص الجماعي
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تم العثور على ملخصات إطفاء التلقائي في كثير من الأحيان تشويه الحقائق أو اختصاصها في المقال.هذا التناقض بين الملخص والنص الأصلي قد أثر بشكل خطير على قابليته للتطبيق.نقترح نموذج تلخيص الحقائق FASUM لاستخراج ودمج العلاقات الواقعية في عملية توليد الموجز عبر انتباه الرسم البياني.ثم نقوم بتصميم نموذج مصحح واقعي FC لتصحيح الأخطاء الواقعية تلقائيا من الملخصات الناتجة عن الأنظمة الحالية.تظهر النتائج التجريبية أن تلخيص حقائق الحقائق يمكن أن تنتج ملخصات إخراج مع اتساق واقعي أعلى مقارنة بالنظام الحالي، ونموذج التصحيح يحسن الاتساق الواقعي الملخصات المعطاة عن طريق تعديل عدد قليل فقط من الكلمات الرئيسية.
Automatic abstractive summaries are found to often distort or fabricate facts in the article. This inconsistency between summary and original text has seriously impacted its applicability. We propose a fact-aware summarization model FASum to extract and integrate factual relations into the summary generation process via graph attention. We then design a factual corrector model FC to automatically correct factual errors from summaries generated by existing systems. Empirical results show that the fact-aware summarization can produce abstractive summaries with higher factual consistency compared with existing systems, and the correction model improves the factual consistency of given summaries via modifying only a few keywords.
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Neural abstractive summarization systems have gained significant progress in recent years. However, abstractive summarization often produce inconsisitent statements or false facts. How to automatically generate highly abstract yet factually correct s
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