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Register a new userIntegrating Semantic Scenario and Word Relations for Abstractive Sentence Summarization
دمج السيناريو الدلالي وعلاقات الكلمات لتلخيص الجملة الجماعية
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تم اعتماد الأساليب القائمة على الرسم البياني مؤخرا لتلخيص نص مبادرة.ومع ذلك، فإن الأساليب القائمة على الرسم البياني الموجودة فقط تنظر فقط في علاقات الكلمات أو معلومات الهيكل، والتي تهمل الارتباط بينهما.في وقت واحد التقاط علاقات الكلمة ومعلومات الهيكل من الجمل، نقترح شبكة الرسم البياني المزدوج الرواية لتلخيص جملة الاختيارات.على وجه التحديد، نقوم أولا بإنشاء رسم بياني للسيناريو الدلالي والكلمة الدلالية الرسم البياني على أساس FRAMENET، وبالتالي تعلم تمثيلاتها وطريقة الانصهار الرسم البياني للتصميم لتعزيز ارتباطها والحصول على تمثيل دلالي أفضل لجيل الملخص.تظهر النتائج التجريبية النموذج لدينا تفوق الأساليب الموجودة في مجموعة بيانات قياسية شعبية، I.E.، GIGAWORD و DUC 2004.
Recently graph-based methods have been adopted for Abstractive Text Summarization. However, existing graph-based methods only consider either word relations or structure information, which neglect the correlation between them. To simultaneously capture the word relations and structure information from sentences, we propose a novel Dual Graph network for Abstractive Sentence Summarization. Specifically, we first construct semantic scenario graph and semantic word relation graph based on FrameNet, and subsequently learn their representations and design graph fusion method to enhance their correlation and obtain better semantic representation for summary generation. Experimental results show our model outperforms existing state-of-the-art methods on two popular benchmark datasets, i.e., Gigaword and DUC 2004.
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