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Register a new userTransformer-based Lexically Constrained Headline Generation
توليد العناوين المعتمد على أساس محول
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تستكشف هذه الورقة متغيرا من أساليب جيلات العناوين التلقائية، حيث يلزم وجود عنوان تم إنشاؤه لتضمين عبارة معينة مثل الشركة أو اسم المنتج. الأساليب السابقة باستخدام النماذج القائمة على المحولات تولد عنوانا يتضمن عبارة معينة من خلال توفير التشفير مع معلومات إضافية مقابلة العبارة المحددة. ومع ذلك، لا يمكن أن تتضمن هذه الأساليب دائما العبارة في العنوان الذي تم إنشاؤه. مستوحاة من الأساليب السابقة القائمة على RNN توليد تسلسل رمزي في الاتجاهات الخلفية والأمام من العبارة المعينة، نقترح طريقة بسيطة قائمة على المحولات التي تضمن تضمين العبارة المحددة في العنوان الرفيع الناتج عن الجودة. ونحن ننظر أيضا في استراتيجية توليد عنوان جديدة تستفيد من ترتيب الجيل القابل للتحكم في المحولات. توضح تجاربنا مع Corpus الأخبار اليابانية أن أساليبنا، التي تضمن إدراج العبارة في العنوان الرئيسي، وتحقيق درجات Rouge مماثلة للأساليب السابقة القائمة على المحولات. نوضح أيضا أن استراتيجية توليدنا تؤدي أفضل من الاستراتيجيات السابقة.
This paper explores a variant of automatic headline generation methods, where a generated headline is required to include a given phrase such as a company or a product name. Previous methods using Transformer-based models generate a headline including a given phrase by providing the encoder with additional information corresponding to the given phrase. However, these methods cannot always include the phrase in the generated headline. Inspired by previous RNN-based methods generating token sequences in backward and forward directions from the given phrase, we propose a simple Transformer-based method that guarantees to include the given phrase in the high-quality generated headline. We also consider a new headline generation strategy that takes advantage of the controllable generation order of Transformer. Our experiments with the Japanese News Corpus demonstrate that our methods, which are guaranteed to include the phrase in the generated headline, achieve ROUGE scores comparable to previous Transformer-based methods. We also show that our generation strategy performs better than previous strategies.
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