Extractive methods have been proven effective in automatic document
summarization. Previous works perform this task by identifying informative
contents at sentence level. However, it is unclear whether performing
extraction at sentence level is the best solution. In this work, we show that
unnecessity and redundancy issues exist when extracting full sentences, and
extracting sub-sentential units is a promising alternative. Specifically, we
propose extracting sub-sentential units based on the constituency parsing tree.
A neural extractive model which leverages the sub-sentential information and
extracts them is presented. Extensive experiments and analyses show that
extracting sub-sentential units performs competitively comparing to full
sentence extraction under the evaluation of both automatic and human
evaluations. Hopefully, our work could provide some inspiration of the basic
extraction units in extractive summarization for future research.

本研究发现在提取文档内容时，提取全句存在不必要和冗余的问题。我们提出了一种基于成分句法分析树的子句单元提取方法，并采用神经萃取模型进行实验分析，发现提取子句单元在人工和自动化评估中表现优异，为未来研究提供了启示。