1 research outputs found
At Which Level Should We Extract? An Empirical Study on Extractive Document Summarization
Extractive methods have proven to be very 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 on the corresponding 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