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Register a new userStructure-Aware Abstractive Conversation Summarization via Discourse and Action Graphs
بنية - تدرك محادثة مبادرة عن طريق خطاب ورسم الرسوم البيانية
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تلقى تلخيص محادثة الجماع اهتماما كبيرا مؤخرا.ومع ذلك، غالبا ما تعاني هذه الملخصات التي تم إنشاؤها من محتوى غير كاف أو زائد أو غير صحيح، ويعزى ذلك إلى حد كبير إلى الخصائص غير المنظمة والمعقدة للتفاعلات البشرية البشرية.تحقيقا لهذه الغاية، نقترح نموذجا صراحة الهياكل الغنية في محادثات للحصول على تلخيص محادثة أكثر دقة ودقيقة، من خلال إدراج علاقات الخطاب الأولى بين الكلام والأربع من الليئات (WHO - ما ") في كلام من خلال الرسوم البيانية المنظمة لتشفير المحادثات بشكل أفضلثم تصميم وحدة فك ترميز متعددة الحبيبات لتوليد ملخصات من خلال الجمع بين جميع مستويات المعلومات.تشير التجارب إلى أن نماذجنا المقترحة تفوقت على الطرق الحديثة والتعميم بشكل جيد في المجالات الأخرى من حيث التقييمات التلقائية والأحكام البشرية.لقد أصدرنا علنا رمزنا في https://github.com/gt-salt/sulture-aware-bart.
Abstractive conversation summarization has received much attention recently. However, these generated summaries often suffer from insufficient, redundant, or incorrect content, largely due to the unstructured and complex characteristics of human-human interactions. To this end, we propose to explicitly model the rich structures in conversations for more precise and accurate conversation summarization, by first incorporating discourse relations between utterances and action triples (who-doing-what'') in utterances through structured graphs to better encode conversations, and then designing a multi-granularity decoder to generate summaries by combining all levels of information. Experiments show that our proposed models outperform state-of-the-art methods and generalize well in other domains in terms of both automatic evaluations and human judgments. We have publicly released our code at https://github.com/GT-SALT/Structure-Aware-BART.
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