Multi-document summarization (MDS) generates a summary from a document set.
Each document in a set describes topic-relevant concepts, while per document
also has its unique contents. However, the document specificity receives little
attention from existing MDS approaches. Neglecting specific information for
each document limits the comprehensiveness of the generated summaries. To solve
this problem, in this paper, we propose to disentangle the specific content
from documents in one document set. The document-specific representations,
which are encouraged to be distant from each other via a proposed orthogonal
constraint, are learned by the specific representation learner. We provide
extensive analysis and have interesting findings that specific information and
document set representations contribute distinctive strengths and their
combination yields a more comprehensive solution for the MDS. Also, we find
that the common (i.e. shared) information could not contribute much to the
overall performance under the MDS settings. Implemetation codes are available
at this https URL

本文提出了一种解决多文档摘要中忽略文档细节的问题的方法，通过从文档集中解开特定的内容并学习文档的特定表示，将特定信息与文档集表示结合，获得更全面的多文档摘要。