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Register a new userControllable Summarization with Constrained Markov Decision Process
لخصية قابلة للتحكم مع عملية قرار ماركوف المقيدة
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مجردة نحن ندرس ملخصات نصية يمكن السيطرة عليها، والتي تتيح للمستخدمين السيطرة على سمة معينة (E.G.، الحد الطول) من الملخصات التي تم إنشاؤها.في هذا العمل، نقترح إطار تدريبي جديد يعتمد على عملية اتخاذ قرار ماركوف المقيد (CMDP)، والتي تتضمن ملاءمة وظيفة المكافأة إلى جانب مجموعة من القيود، لتسهيل سيطرة تلخيص أفضل.تشجع الوظيفة المكافأة على جيل تشبه المرجع الخطي البشري، في حين يتم استخدام القيود لتمنع بشكل صريح الملخصات التي تم إنشاؤها من انتهاك الاحتياجات التي يفرضها المستخدم.يمكن تطبيق إطارنا للتحكم في السمات المهمة من التلخيص، بما في ذلك الطول والكيانات المغطاة والتجريد، حيث أننا نضع قيود محددة لكل من هذه الجوانب.تبين تجارب واسعة النطاق على المعايير الشعبية أن إطار عمل مؤتمر الأطراف الخاص ب CMDP يساعد في توليد ملخصات إعلامية مع الامتثال لمتطلبات سمة معينة
Abstract We study controllable text summarization, which allows users to gain control on a particular attribute (e.g., length limit) of the generated summaries. In this work, we propose a novel training framework based on Constrained Markov Decision Process (CMDP), which conveniently includes a reward function along with a set of constraints, to facilitate better summarization control. The reward function encourages the generation to resemble the human-written reference, while the constraints are used to explicitly prevent the generated summaries from violating user-imposed requirements. Our framework can be applied to control important attributes of summarization, including length, covered entities, and abstractiveness, as we devise specific constraints for each of these aspects. Extensive experiments on popular benchmarks show that our CMDP framework helps generate informative summaries while complying with a given attribute's requirement.1
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