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Register a new userSummEval: Re-evaluating Summarization Evaluation
Summeval: إعادة تقييم تقييم التلخيص
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خلاص ندرة الدراسات الشاملة الحديثة بشأن مقاييس التقييم لتلخيص النص ونقص توافق الآراء بشأن بروتوكولات التقييم لا تزال تمنع التقدم المحرز. نحن نتطلع إلى أوجه القصور الموجودة لأساليب تقييم التلخيص على طول خمسة أبعاد: 1) نعيد تقييم 14 مقيط تقييم تلقائي في أزياء شاملة ومتسقة باستخدام مخرجات نموذج تلخيص العصبي جنبا إلى جنب مع التوضيحات البشرية الخبراء والجمهور؛ 2) نحن معيارون باستمرار 23 نماذج تلخيص حديثة باستخدام مقاييس التقييم التلقائي المذكورة أعلاه؛ 3) نحن نكذب أكبر مجموعة من الملخصات الناتجة عن النماذج المدربة على مجموعة بيانات CNN / DailyMail الأخبار ومشاركتها في شكل موحد؛ 4) نحن نفرا ومشاركة مجموعة أدوات توفر واجهة برمجة تطبيقية قابلة للتوسيع وغير موحدة لتقييم نماذج تلخيص عبر مجموعة واسعة من المقاييس التلقائية؛ 5) نحن نكذب ومشاركة أكبر وأكثرها تنوعا، من حيث الأنواع النموذجية، وجمع الأحكام البشرية من الملخصات التي تم إنشاؤها النموذجية على مجموعة بيانات CNN / Daily Mail المشروحة من قبل كلا من قضاة الخبراء والعاملين بمصدر الحشد. نأمل أن يساعد هذا العمل في تعزيز بروتوكول تقييم أكثر اكتمالا لتلخيص النص وكذلك البحث المسبق في تطوير مقاييس التقييم التي ترتبط بأحكام الإنسان بشكل أفضل.
Abstract The scarcity of comprehensive up-to-date studies on evaluation metrics for text summarization and the lack of consensus regarding evaluation protocols continue to inhibit progress. We address the existing shortcomings of summarization evaluation methods along five dimensions: 1) we re-evaluate 14 automatic evaluation metrics in a comprehensive and consistent fashion using neural summarization model outputs along with expert and crowd-sourced human annotations; 2) we consistently benchmark 23 recent summarization models using the aforementioned automatic evaluation metrics; 3) we assemble the largest collection of summaries generated by models trained on the CNN/DailyMail news dataset and share it in a unified format; 4) we implement and share a toolkit that provides an extensible and unified API for evaluating summarization models across a broad range of automatic metrics; and 5) we assemble and share the largest and most diverse, in terms of model types, collection of human judgments of model-generated summaries on the CNN/Daily Mail dataset annotated by both expert judges and crowd-source workers. We hope that this work will help promote a more complete evaluation protocol for text summarization as well as advance research in developing evaluation metrics that better correlate with human judgments.
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