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Register a new userEASE: Extractive-Abstractive Summarization End-to-End using the Information Bottleneck Principle
سهولة: ملخص مبادرة الاستخراجية - إلى النهاية باستخدام مبدأ الاختناق المعلومات
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تتفوق أنظمة تلخيص التلخيص الحالية على نظرائهم المستخرجين، لكن اعتمادهم على نطاق واسع يمنعهم الافتقار المتأصل إلى الترجمة الشفوية. أنظمة تلخيص الاستخراجية، على الرغم من أنه قابل للتفسير، تعاني من التكرار وقلة الاتساق المحتمل. لتحقيق أفضل ما في العالمين، نقترح سهولة، وهو إطار خارجي - مبادرة ينشئ ملخصات إغراقية موجزة يمكن تتبعها مرة أخرى إلى ملخص مستخرج. يمكن تطبيق إطارنا على أي مشكلة توليد نصية قائمة على الأدلة ويمكن أن تستوعب النماذج المحددة مسبقا في بنية بسيطة. نستخدم مبدأ معلومات عنق المعلومات لتدريب الاستخلاص والتجريد المشترك في أزياء نهاية إلى نهاية. مستوحاة من البحث السابق الذي يستخدمه البشر إطارا من مرحلتين لتلخيص المستندات الطويلة (Jing و McKeown، 2000)، فإن إطار عملائنا أولا يستخرج كمية محددة مسبقا من الأدلة التي تمتد ثم يولد ملخصا باستخدام الأدلة فقط. باستخدام التقييمات التلقائية والبشرية، نوضح أن الملخصات التي تم إنشاؤها أفضل من خطوط الأساسيات الاستخراجية والاستخراجية الدخرية.
Current abstractive summarization systems outperform their extractive counterparts, but their widespread adoption is inhibited by the inherent lack of interpretability. Extractive summarization systems, though interpretable, suffer from redundancy and possible lack of coherence. To achieve the best of both worlds, we propose EASE, an extractive-abstractive framework that generates concise abstractive summaries that can be traced back to an extractive summary. Our framework can be applied to any evidence-based text generation problem and can accommodate various pretrained models in its simple architecture. We use the Information Bottleneck principle to jointly train the extraction and abstraction in an end-to-end fashion. Inspired by previous research that humans use a two-stage framework to summarize long documents (Jing and McKeown, 2000), our framework first extracts a pre-defined amount of evidence spans and then generates a summary using only the evidence. Using automatic and human evaluations, we show that the generated summaries are better than strong extractive and extractive-abstractive baselines.
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