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Register a new userKnowledge and Keywords Augmented Abstractive Sentence Summarization
المعرفة والكلمات الرئيسية المعزز جملة مبادرة تلخيص
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في هذه الورقة، ندرس تلخيص الجملة المبادرة.هناك ميزان معلومات أساسية يمكن أن تؤثر على جودة تلخيص الأخبار، والتي هي الكلمات الرئيسية للموضوع والهيكل المعرفي لنص الأخبار.علاوة على ذلك، فإن تشفير المعرفة الموجودة لديها أداء ضعيف في هيكل المعرفة بالقضاء السريع.بالنظر إلى هذه، نقترح KAS، ومعرفة رواية وتحويل الكلمات الرئيسية المعزز بإطار تلخيص الجملة المبادرة.يتم استخدام Tri-Encoders لإدماج سياقات النص الأصلي وهيكل المعرفة وموضوع الكلمات الرئيسية في وقت واحد، مع بنية معرفة خطية خاصة.التقييمات التلقائية والبشرية تثبت أن KAS تحقق أفضل العروض.
In this paper, we study the abstractive sentence summarization. There are two essential information features that can influence the quality of news summarization, which are topic keywords and the knowledge structure of the news text. Besides, the existing knowledge encoder has poor performance on sparse sentence knowledge structure. Considering these, we propose KAS, a novel Knowledge and Keywords Augmented Abstractive Sentence Summarization framework. Tri-encoders are utilized to integrate contexts of original text, knowledge structure and keywords topic simultaneously, with a special linearized knowledge structure. Automatic and human evaluations demonstrate that KAS achieves the best performances.
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