论文标题
自适应光束搜索以增强设备上的抽象摘要
Adaptive Beam Search to Enhance On-device Abstractive Summarization
论文作者
论文摘要
我们以短信，文档，语音消息等形式的智能手机收到几个基本更新，这些更新被埋在内容的混乱之下。如果不浏览全部内容，我们通常不会意识到关键信息。 SMS通知有时会通过了解信息的意义来有所帮助，但是，它们仅提供开始内容的预览。解决此问题的一种方法是拥有一个可以调整和总结各种来源数据的单一有效模型。在本文中，我们第一次解决了这个问题，并首次提出了一种新型的自适应束搜索，以提高可以应用于SMS，语音消息并可以将其扩展到文档的设备抽象性摘要的质量。据我们所知，这是提出的第一个在设备上的抽象性摘要管道，可以适应多个数据源，以解决用户的隐私问题，与将数据发送到服务器的大多数现有汇总系统相比。我们使用知识蒸馏将模型尺寸减少了30.9％，并表明，与BERT相比，该模型的内存足迹较少97.6％的含量少97.6％。
We receive several essential updates on our smartphones in the form of SMS, documents, voice messages, etc. that get buried beneath the clutter of content. We often do not realize the key information without going through the full content. SMS notifications sometimes help by giving an idea of what the message is about, however, they merely offer a preview of the beginning content. One way to solve this is to have a single efficient model that can adapt and summarize data from varied sources. In this paper, we tackle this issue and for the first time, propose a novel Adaptive Beam Search to improve the quality of on-device abstractive summarization that can be applied to SMS, voice messages and can be extended to documents. To the best of our knowledge, this is the first on-device abstractive summarization pipeline to be proposed that can adapt to multiple data sources addressing privacy concerns of users as compared to the majority of existing summarization systems that send data to a server. We reduce the model size by 30.9% using knowledge distillation and show that this model with a 97.6% lesser memory footprint extracts the same or more key information as compared to BERT.
