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Register a new userLearn to Copy from the Copying History: Correlational Copy Network for Abstractive Summarization
تعلم النسخ من تاريخ النسخ: شبكة النسخ المرصعة لتلخيص إطفاء
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كان لآلية النسخ نجاحا كبيرا في تلخيص الجماعي، مما يسهل النماذج لنسخ الكلمات مباشرة من نص الإدخال إلى ملخص الإخراج. يعمل Works الموجود في الغالب اهتماما في تشفير التشفير، والتي تنطبق النسخ في كل مرة خطوة في الوقت المستقل من السابقين. ومع ذلك، فقد يؤدي ذلك في بعض الأحيان إلى نسخ غير مكتمل. في هذه الورقة، نقترح خطة نسخ رواية تسمى شبكة النسخ المصنعة للتصوير (COOCONET) تعزز آلية النسخ القياسية عن طريق تتبع تاريخ النسخ. وبالتالي يستفيد من توزيعات النسخ المسبقة، وفي كل خطوة في كل مرة، يشجع النموذج بشكل صريح على نسخ كلمة الإدخال ذات الصلة بالوحدة المنسوخة مسبقا. بالإضافة إلى ذلك، نقوم بتعزيز جوز الهند من خلال التدريب المسبق مع شركة سورية مناسبة محاكاة سلوكيات النسخ. تظهر النتائج التجريبية أن CocoNet يمكن أن نسخ أكثر دقة وتحقق أدائا جديدا جديدا في معايير تلخيص، بما في ذلك CNN / Dailymail لتلخيص الأخبار وسامسوم لتلخيص الحوار. سيتم توفير التعليمات البرمجية ونقطة التفتيش علانية.
The copying mechanism has had considerable success in abstractive summarization, facilitating models to directly copy words from the input text to the output summary. Existing works mostly employ encoder-decoder attention, which applies copying at each time step independently of the former ones. However, this may sometimes lead to incomplete copying. In this paper, we propose a novel copying scheme named Correlational Copying Network (CoCoNet) that enhances the standard copying mechanism by keeping track of the copying history. It thereby takes advantage of prior copying distributions and, at each time step, explicitly encourages the model to copy the input word that is relevant to the previously copied one. In addition, we strengthen CoCoNet through pre-training with suitable corpora that simulate the copying behaviors. Experimental results show that CoCoNet can copy more accurately and achieves new state-of-the-art performances on summarization benchmarks, including CNN/DailyMail for news summarization and SAMSum for dialogue summarization. The code and checkpoint will be publicly available.
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Replica technique remain most commonly used technique for
measuring the marginal fit in the medical literature, the silicone
rubber addition is considered most accurate and least changing
dimensions.
Aim of study to compare the accuracy and simpl
icity of traditional
replica technique and modified replica technique to measure
marginal fit of zirconium posterior bridges manufactured by
CAD\CAM. After removal of first lower molar from TYPODENT
(Frasco, Tettnang, Germany), the second lower molar and premolar
neighboring were prepared commensurate with full ceramic
restoration. Final impression of this bridge was taken by silicone
rubber addition (Zhermack Elite HD+) with the aid of custom tray.
The impression was condensed with amalgam to make the main
metal cast. Fifteen custom acrylic resin trays were used for fabricate
fifteen impressions of main metal cast that were used for fabricate
15 zirconium bridges by CAD\CAM technique According to the
manufacturer's instructions. Tow rubber thicknesses were taken for
the marginal fit of each bridge over its cast gypsum by used light
silicone rubber addition. Then the first rubber thickness was treated
by developed replica technique to form the Group (A) of the study
and the second rubber thickness was treated by replica technique to
form the group (B) of the study. Depending on T-test, no significant
difference between two techniques was detected. We conclude that
developed replica technique reliable technique to study and measure the marginal gap of bridge and crown, and it is statistically more accurate than the traditional technique.
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Abstractive summarization models heavily rely on copy mechanisms, such as the pointer network or attention, to achieve good performance, measured by textual overlap with reference summaries. As a result, the generated summaries stay close to the form
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