基于多任务学习的生成文本摘要研究

Multi-task Learning for Abstractive Text Summarization

基于注意力机制的编码-解码模型的神经网络具有很好的生成文本摘要能力。但是,这些模型在生成过程中很难控制,这导致在生成文本中缺少关键信息。一些关键信息,例如时间,地点和人物,对于人类理解主要内容是必不可少的。本文提出了一个基于多任务学习框架的用于生成文本摘要的关键信息指南网络。主要思想是以端到端的方式自动提取人们最需要的关键信息,并用其指导生成过程,从而获得更符合人类需求的摘要。在本文提出的模型中,文档被编码为两个部分:普通文档编码器的编码结果和关键信息的编码,关键信息包括关键句和关键词。引入了多任务学习框架以获得更先进的端到端模型。为了融合关键信息,提出了一种多视角注意指南网络,以获取源文本和关键信息的向量。另外,向量被合并到生成模块中以指导摘要生成的过程。本文在CNN与Daily Mail数据集上评估了模型,实验结果表明此模型有重大改进。

The neural network of the encoding-decoding model based on the attention mechanism has a good ability to generate text summaries.However,these models are difficult to control during the generation process,which leads to the lack of key information in the generated text.Some key information,such as time,place,and people,is essential for humans to understand the main content.This paper proposes a key information guide network for generating text summaries based on a multi-task learning framework.The main idea is to automatically extract the key information that people need most in an end-to-end manner,and use it to guide the generation process,so as to obtain a summary that is more in line with human needs.In the model proposed in this paper,the document is coded into two parts:the coding result of the ordinary document encoder and the coding of key information.The key information includes key sentences and keywords.A multi-task learning framework is introduced to obtain a more advanced end-toend model.In order to fuse key information,a multi-perspective attention guide network is proposed to obtain the vector of source text and key information.In addition,vectors are incorporated into the generation module to guide the process of abstract generation.This paper evaluates the model on the CNN and Daily Mail datasets,and the experimental results show that this model has significant improvements.