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Register a new userForumSum: A Multi-Speaker Conversation Summarization Dataset
Forumsumum: محادثة محادثة متعددة المتكلم
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كانت جودة تلخيص الجماعة لديها تحسينات كبيرة منذ تقنيات محاكاة اللغة الأخيرة.ومع ذلك، هناك حاليا نقص في مجموعات البيانات للاحتياجات المتزايدة لتطبيقات تلخيص المحادثة.وبالتالي نحن جمعنا منتديات، مجموعة بيانات ملخصة محادثة متنوعة وعالية الجودة مع ملخصات مكتوبة بشرية.تتم جمع المحادثات في DiversionMum DataSet من مجموعة واسعة من منتديات الإنترنت.لجعل مجموعة البيانات قابلة للتوسيع بسهولة، نقوم أيضا بإصدار عملية إنشاء DataSet.تظهر تجاربنا أن النماذج المدربة على Forumsum لديها أفضل صفر - لقدرة على تحويل القليل من الطوابق إلى مجموعات البيانات الأخرى من بيانات ملخصات الدردشة الكبيرة الحالية Samsum.نظهر أيضا أن استخدام Corpus Corpustation للمحدثين يحسن ما قبل التدريب على تحسين جودة نموذج تلخيص الدردشة.
Abstractive summarization quality had large improvements since recent language pretraining techniques. However, currently there is a lack of datasets for the growing needs of conversation summarization applications. Thus we collected ForumSum, a diverse and high-quality conversation summarization dataset with human written summaries. The conversations in ForumSum dataset are collected from a wide variety of internet forums. To make the dataset easily expandable, we also release the process of dataset creation. Our experiments show that models trained on ForumSum have better zero-shot and few-shot transferability to other datasets than the existing large chat summarization dataset SAMSum. We also show that using a conversational corpus for pre-training improves the quality of the chat summarization model.
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