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Register a new userA Finer-grain Universal Dialogue Semantic Structures based Model For Abstractive Dialogue Summarization
الهياكل الدلوية الشاملة للحبوب الدلالي
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على الرغم من أن نماذج التلخيص المحظورة حققت نتائج مثيرة للإعجاب في مهام تلخيص المستندات، فإن أدائها على نمذجة الحوار أقل مرضية بأقل قدر ممكن بسبب الأساليب النفطية والمتصلة لترميز الحوار.لمعالجة هذا السؤال، نقترح نموذجا نزايدا للنماذج القائمة على المحولات في النهاية لتلخيص الحوار الجماعي الذي يرفع الهياكل الدلالية الدلالية الشديدة الحبوب إلى الحوار النموذجي وتوليد ملخصات أفضل.تجارب في برنامج Samsum DataSet الذي يجد يتفوق على مناهج تلخيص الحوار المختلفة وتحقق نتائج Rouge الحديثة (SOTA) الجديدة.أخيرا، نطبق أن نتمكن من العثور على سيناريو أكثر تعقيدا، مما يدل على تقلص نموذجنا.ونحن نطلق أيضا شفرة المصدر لدينا.
Although abstractive summarization models have achieved impressive results on document summarization tasks, their performance on dialogue modeling is much less satisfactory due to the crude and straight methods for dialogue encoding. To address this question, we propose a novel end-to-end Transformer-based model FinDS for abstractive dialogue summarization that leverages Finer-grain universal Dialogue semantic Structures to model dialogue and generates better summaries. Experiments on the SAMsum dataset show that FinDS outperforms various dialogue summarization approaches and achieves new state-of-the-art (SOTA) ROUGE results. Finally, we apply FinDS to a more complex scenario, showing the robustness of our model. We also release our source code.
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