# At Which Level Should We Extract? An Empirical Analysis on Extractive Document Summarization

arXiv: https://arxiv.org/abs/2004.02664 · HTML: https://mlsanity.com/paper/2004.02664/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2020.

## Built on by (most cited first, 3 of 3)

- [Exploring Optimal Granularity for Extractive Summarization of Unstructured Health Records: Analysis of the Largest Multi-Institutional Archive of Health Records in Japan](https://mlsanity.com/paper/2209.10041/) (2022)
- [Generating Tips from Song Reviews: A New Dataset and Framework](https://mlsanity.com/paper/2205.06985/) (2022)
- [The Sum of All Summaries: PROPALIGN for Graded Proposition Salience Evaluation](https://mlsanity.com/paper/2610.19387/) (2026)

## Abstract (arXiv)

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.