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Register a new userCapturing Speaker Incorrectness: Speaker-Focused Post-Correction for Abstractive Dialogue Summarization
التقاط غير صحيح المتكلم: تصحيح ما بعد التركيز على السماعات لتلخيص الحوار الجماعي
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في هذه الورقة، نركز على تحسين جودة الملخص الذي تم إنشاؤه بواسطة أنظمة تلخيص الحوار المبشور العصبي.على الرغم من أن طرازات اللغة المدربة مسبقا تولد نتائج رائعة واعدة، إلا أنها لا تزال تحديا لتلخيص محادثة المشاركين المتعددين منذ أن تتضمن الملخص وصفا للوضع العام وإجراءات كل مكبر صوت.تقترح هذه الورقة استراتيجيات ذات إشراف ذاتي لتصحيح ما بعد تركز على المتكلم في تلخيص حوار المبادرة.على وجه التحديد، تميز نموذجنا أولا أي نوع من تصحيح المتكلم مطلوب في مشروع ملخص ثم يولد ملخص منقح وفقا للنوع المطلوب.تظهر النتائج التجريبية أن أسلوبنا المقترح بتصحيح مشاريع الملخصات بشكل كاف، ويتم تحسين الملخصات المنقحة بشكل كبير في كل من التقييمات الكمية والنوعية.
In this paper, we focus on improving the quality of the summary generated by neural abstractive dialogue summarization systems. Even though pre-trained language models generate well-constructed and promising results, it is still challenging to summarize the conversation of multiple participants since the summary should include a description of the overall situation and the actions of each speaker. This paper proposes self-supervised strategies for speaker-focused post-correction in abstractive dialogue summarization. Specifically, our model first discriminates which type of speaker correction is required in a draft summary and then generates a revised summary according to the required type. Experimental results show that our proposed method adequately corrects the draft summaries, and the revised summaries are significantly improved in both quantitative and qualitative evaluations.
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Dialogue Act (DA) classification is the task of classifying utterances with respect to the function they serve in a dialogue. Existing approaches to DA classification model utterances without incorporating the turn changes among speakers throughout t
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