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Register a new userTopic-Aware Contrastive Learning for Abstractive Dialogue Summarization
تدرك موضوع التعلم المتعاقل لتلخيص حوار المبادرة
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على عكس النص المنظم جيدا، مثل التقارير الإخبارية ومقالات الموسوعة، غالبا ما يأتي محتوى الحوار من محاورين أو أكثر، وتبادل المعلومات مع بعضها البعض. في مثل هذا السيناريو، يمكن أن يختلف موضوع المحادثة عند التقدم والمعلومات الأساسية لموضوع معين في كثير من الأحيان متناثرة عبر مختلف الكلام من المتكلمين المختلفة، مما يطرح التحديات التي تلخص التخلص من الحوارات بشكل مجردة. لالتقاط معلومات الموضوع المختلفة للمحادثة والحقائق البارزة على الموضوعات التي تم الاستيلاء عليها، يقترح هذا العمل أهدافا للتعلم المتعاواة على علم الموضوع، وهي اكتشاف الاتساق وأهداف الجيل الموجزة الفرعية، والتي من المتوقع أن تقوم بها ضمنيا في تغيير موضوع وتغيير الموضوع تحديات تثير المعلومات لمهمة تلخيص الحوار. يتم تأطير الأهداف المقنعة المقترحة بمثابة مهام مساعدة لمهمة تلخيص الحوار الأساسي، المتحدة عبر استراتيجية تحديث معلمة بديلة. توضح تجارب واسعة النطاق على مجموعات البيانات القياسية أن الطريقة البسيطة المقترحة تتفوق بشكل كبير على خطوط الأساس القوية وتحقق أداء جديد من بين الفني. الرمز والنماذج المدربة متاحة للجمهور عبر.
Unlike well-structured text, such as news reports and encyclopedia articles, dialogue content often comes from two or more interlocutors, exchanging information with each other. In such a scenario, the topic of a conversation can vary upon progression and the key information for a certain topic is often scattered across multiple utterances of different speakers, which poses challenges to abstractly summarize dialogues. To capture the various topic information of a conversation and outline salient facts for the captured topics, this work proposes two topic-aware contrastive learning objectives, namely coherence detection and sub-summary generation objectives, which are expected to implicitly model the topic change and handle information scattering challenges for the dialogue summarization task. The proposed contrastive objectives are framed as auxiliary tasks for the primary dialogue summarization task, united via an alternative parameter updating strategy. Extensive experiments on benchmark datasets demonstrate that the proposed simple method significantly outperforms strong baselines and achieves new state-of-the-art performance. The code and trained models are publicly available via .
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We study generating abstractive summaries that are faithful and factually consistent with the given articles. A novel contrastive learning formulation is presented, which leverages both reference summaries, as positive training data, and automaticall
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