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.

本文提出了ACM，一种属性条件化的多文档摘要方法，该方法在解决多篇文档信息合成和提取显著信息的问题时，通过引入属性条件化模块来消除对于一个特定主题可能存在的情感、偏见或主观信息的冲突，相较于其他基线模型具有更好的ROUGE得分和更高的，流畅性、信息量以及减少重复性等优点，在人工标注分析研究中也得到了验证。

ACM——用于抽象化多文档摘要的属性调节