论文标题
ACM-抽象多文档摘要的属性条件
ACM -- Attribute Conditioning for Abstractive Multi Document Summarization
论文作者
论文摘要
抽象性多文档摘要通过基本序列发展为一项任务，从基于变压器和图形技术的序列方法来发展。这些方法中的每一种主要集中在多文档信息综合的问题和基于注意力的方法以提取显着信息的问题上。在单个文档摘要中不普遍的多文档摘要带来的挑战是需要有效地总结有关给定主题可能具有冲突的极性，情感或主观信息的多个文档。在本文中，我们提出了ACM，属性条件多文档摘要，该模型结合了属性调节模块，以通过调节输出摘要中的某个属性来解除冲突的信息。这种方法显示了基线多文档摘要方法的胭脂评分的强劲增长，并显示了通过人类注释分析研究所示的流利性，信息性和降低重复性的增长。
Abstractive multi document summarization has evolved as a task through the basic sequence to sequence approaches to transformer and graph based techniques. Each of these approaches has primarily focused on the issues of multi document information synthesis and attention based approaches to extract salient information. A challenge that arises with multi document summarization which is not prevalent in single document summarization is the need to effectively summarize multiple documents that might have conflicting polarity, sentiment or subjective information about a given topic. In this paper we propose ACM, attribute conditioned multi document summarization,a model that incorporates attribute conditioning modules in order to decouple conflicting information by conditioning for a certain attribute in the output summary. This approach shows strong gains in ROUGE score over baseline multi document summarization approaches and shows gains in fluency, informativeness and reduction in repetitiveness as shown through a human annotation analysis study.
