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Register a new userControllable Neural Dialogue Summarization with Personal Named Entity Planning
لخصي الحوار العصبي قابل للتحكم مع تخطيط الكيانات المسماة الشخصية
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في هذه الورقة، نقترح إطار جيل عصبي قابل للتحكم يمكن أن توجه بمرونة تلخيص الحوار مع تخطيط الكيانات المسماة الشخصية. يتم تعديل التسلسلات الشرطية لتحديد أنواع المعلومات أو منظور التركيز عند تشكيل ملخصات لمعالجة المشكلة الخاضعة للحدود في مهام التلخصات. يدعم هذا الإطار نوعين من حالات الاستخدام: (1) منظور شامل، وهو حالة غرض لأغراض عامة مع عدم تحديد تفضيل المستخدم، بالنظر إلى نقاط موجزة من جميع محطات المحادثة والأشخاص المذكورين؛ (2) منظور التركيز، ضع الملخص بناء على كيان شخصي محدد من قبل المستخدم، والتي يمكن أن تكون واحدة من المحاورين أو أحد الأشخاص المذكورين في المحادثة. أثناء التدريب، استغلنا تخطيط حدوثها للكيانات المسماة الشخصية ومعلومات العناية الأساسية لتحسين الاتساق الزمني وتقليل الهلوسة في الجيل العصبي. تظهر النتائج التجريبية أن إطار عملنا المقترح يولد ملخصات بطلاقة ومتسقة في الواقع بموجب ضوابط التخطيط المختلفة باستخدام المقاييس الموضوعية والتقييمات البشرية.
In this paper, we propose a controllable neural generation framework that can flexibly guide dialogue summarization with personal named entity planning. The conditional sequences are modulated to decide what types of information or what perspective to focus on when forming summaries to tackle the under-constrained problem in summarization tasks. This framework supports two types of use cases: (1) Comprehensive Perspective, which is a general-purpose case with no user-preference specified, considering summary points from all conversational interlocutors and all mentioned persons; (2) Focus Perspective, positioning the summary based on a user-specified personal named entity, which could be one of the interlocutors or one of the persons mentioned in the conversation. During training, we exploit occurrence planning of personal named entities and coreference information to improve temporal coherence and to minimize hallucination in neural generation. Experimental results show that our proposed framework generates fluent and factually consistent summaries under various planning controls using both objective metrics and human evaluations.
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