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Register a new userMediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization
MediaSum: مجموعة بيانات مقابلة وسائط واسعة النطاق لتلخيص الحوار
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تقدم هذه الورقة MediaSum، مجموعة بيانات مقابلة الوسائط على نطاق واسع تتكون من نصوص 463.6 كيلو بايت مع ملخصات إبتياج.لإنشاء هذه البيانات، نجمع مخالفات المقابلة من NPR و CNN وتوظيف نظرة عامة وأوصاف موضوع كملخصات.مقارنة مع الشركة العامة القائمة للحصول على تلخيص الحوار، فإن DataSet لدينا هي أمر من حيث الحجم ويحتوي على محادثات متعددة الأحزاب المعقدة من مجالات متعددة.نقوم بإجراء تحليل إحصائي لإظهار التحيز الموضعي الفريد المعروض في نصوص المقابلات التلفزيونية والإذاعية.نظهر أيضا أن MediaSum يمكن استخدامه في تعلم التعلم لتحسين أداء نموذج على مهام تلخيص حوار أخرى.
This paper introduces MediaSum, a large-scale media interview dataset consisting of 463.6K transcripts with abstractive summaries. To create this dataset, we collect interview transcripts from NPR and CNN and employ the overview and topic descriptions as summaries. Compared with existing public corpora for dialogue summarization, our dataset is an order of magnitude larger and contains complex multi-party conversations from multiple domains. We conduct statistical analysis to demonstrate the unique positional bias exhibited in the transcripts of televised and radioed interviews. We also show that MediaSum can be used in transfer learning to improve a model's performance on other dialogue summarization tasks.
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