شهدت السنوات الأخيرة ازدهارا من أعمال جيل المفاتيح العصبي (KPG)، بما في ذلك إصدار العديد من البيانات واسعة النطاق ومجموعة من النماذج الجديدة لمعالجةها.زاد أداء النموذج على مهام KPG بشكل كبير مع أبحاث التعلم العميق المتطور.ومع ذلك، يفتقر إلى مقارنة شاملة بين مختلف التصاميم النموذجية، والتحقيق الشامل على العوامل ذات الصلة التي قد تؤثر على أداء تعميم نظام KPG.في هذه الدراسة التجريبية، نهدف إلى ملء هذه الفجوة من خلال توفير نتائج تجريبية واسعة وتحليل العوامل الأكثر أهمية التي تؤثر على تعميم نماذج KPG.نأمل أن تساعد هذه الدراسة في توضيح بعض الشكوك المحيطة بمهمة KPG وتسهيل البحث في المستقبل حول هذا الموضوع.
Recent years have seen a flourishing of neural keyphrase generation (KPG) works, including the release of several large-scale datasets and a host of new models to tackle them. Model performance on KPG tasks has increased significantly with evolving deep learning research. However, there lacks a comprehensive comparison among different model designs, and a thorough investigation on related factors that may affect a KPG system's generalization performance. In this empirical study, we aim to fill this gap by providing extensive experimental results and analyzing the most crucial factors impacting the generalizability of KPG models. We hope this study can help clarify some of the uncertainties surrounding the KPG task and facilitate future research on this topic.
References used
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